ALBERT

Description: Objective: We present the framework for wearable joint rehabilitation assessment following musculoskeletal injury. We propose a multimodal sensing (i.e., contact based and airborne measurement of joint acoustic emission) system for at-home monitoring. Methods: We used three types of microphones—electret, MEMS, and piezoelectric film microphones—to obtain joint sounds in healthy collegiate athletes during unloaded flexion/extension, and we evaluated the robustness of each microphone's measurements via: 1) signal quality and 2) within-day consistency. Results: First, air microphones acquired higher quality signals than contact microphones (signal-to-noise-and-interference ratio of 11.7 and 12.4 dB for electret and MEMS, respectively, versus 8.4 dB for piezoelectric). Furthermore, air microphones measured similar acoustic signatures on the skin and 5 cm off the skin (∼4.5× smaller amplitude). Second, the main acoustic event during repetitive motions occurred at consistent joint angles (intra-class correlation coefficient ICC(1, 1) = 0.94 and ICC(1, k) = 0.99). Additionally, we found that this angular location was similar between right and left legs, with asymmetry observed in only a few individuals. Conclusion: We recommend using air microphones for wearable joint sound sensing; for practical implementation of contact microphones in a wearable device, interface noise must be reduced. Importantly, we show that airborne signals can be measured consistently and that healthy left and right knees often produce a similar pattern in acoustic emissions. Significance: These proposed methods have the potential for enabling knee joint acoustics measurement outside the clinic/lab and permitting long-term monitoring of knee health for patients rehabilitating an acute knee joint injury.

Description: As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

Description: Platelet-rich plasma (PRP) is a volume of autologous plasma that has a higher platelet concentration above baseline. It has already been approved as a new therapeutic modality and investigated in clinics, such as bone repair and regeneration, and oral surgery, with low cost-effectiveness ratio. At present, PRP is mostly prepared using a centrifuge. However, this method has several shortcomings, such as long preparation time (30 min), complexity in operation, and contamination of red blood cells (RBCs). In this paper, a new PRP preparation approach was proposed and tested. Ultrasound waves (4.5 MHz) generated from piezoelectric ceramics can establish standing waves inside a syringe filled with the whole blood. Subsequently, RBCs would accumulate at the locations of pressure nodes in response to acoustic radiation force, and the formed clusters would have a high speed of sedimentation. It is found that the PRP prepared by the proposed device can achieve higher platelet concentration and less RBCs contamination than a commercial centrifugal device, but similar growth factor (i.e., PDGF-ββ). In addition, the sedimentation process under centrifugation and sonication was simulated using the Mason–Weaver equation and compared with each other to illustrate the differences between these two technologies and to optimize the design in the future. Altogether, ultrasound method is an effective method of PRP preparation with comparable outcomes as the commercially available centrifugal products.

Description: A new wireless sensor was designed, fabricated, and applied for in situ monitoring of tensile force at a wound site. The sensor was comprised of a thin strip of magnetoelastic material with its two ends connected to suture threads for securing the sensor across a wound repair site. Since the sensor was remotely interrogated by applying an ac magnetic field and capturing the resulting magnetic field, it did not require direct wire connections to an external device or internal battery for long-term use. Due to its magnetoelastic property, the application of a tensile force changed the magnetic permeability of the sensor, altering the amplitude of the measured magnetic field. This study presents two sensor designs: one for high and one for low-force ranges. A sensor was fabricated by directly adhering the magnetoelastic strip to the suture. This sensor showed good sensitivity at low force, but its response saturated at about 1.5 N. To monitor high tensile force, the magnetoelastic strip was attached to a metal strip for load sharing. The suture thread was attached to the both ends of the metal strip so only a fraction of the applied force was directed to the sensor, allowing it to exhibit good sensitivity even at 44.5 N. The sensor was applied to two ex vivo models: a sutured section of porcine skin and a whitetail deer Achilles tendon. The results demonstrate the potential for in vivo force monitoring at a wound repair site.

Description: This study presents a precise way to detect the third ( $S_{3}$ ) heart sound, which is recognized as an important indication of heart failure, based on nonlinear single decomposition and time–frequency localization. The detection of the $S_{3}$ is obscured due to its significantly low energy and frequency. Even more, the detected $S_{3}$ may be misunderstood as an abnormal second heart sound with a fixed split, which was not addressed in the literature. To detect such $S_{3}$ , the Hilbert vibration decomposition method is applied to decompose the heart sound into a certain number of subcomponents while intactly preserving the phase information. Thus, the time information of all of the decomposed components are unchanged, which further expedites the identification and localization of any module/section of a signal properly. Next, the proposed localization step is applied to the decomposed subcomponents by using smoothed pseudo Wigner–Ville distribution followed by the reassignment method. Finally, based on the positional information, the $S_{3}$ is distinguished and confirmed by measuring time delays between the $S_{2}$ and $S_{3}$ . In total, 82 sets of cardiac cycles collected from different databases including Texas Heart Institute database are examined for evaluation of the proposed method. The result analysis shows that the proposed method can detect the $S_{3}$ correctly, even when the - ormalized temporal energy of $S_{3}$ is larger than 0.16, and the frequency of those is larger than 34 Hz. In a performance analysis, the proposed method demonstrates that the accuracy rate of $S_{3}$ detection is as high as 93.9%, which is significantly higher compared with the other methods. Such findings prove the robustness of the proposed idea for detecting substantially low-energized $S_{3}$ .

Description: Electromagnetic (EM) tracking systems are highly susceptible to field distortion. The interference can cause measurement errors up to a few centimeters in clinical environments, which limits the reliability of these systems. Unless corrected for, this measurement error imperils the success of clinical procedures. It is therefore fundamental to dynamically calibrate EM tracking systems and compensate for measurement error caused by field distorting objects commonly present in clinical environments. We propose to combine a motion model with observations of redundant EM sensors and compensate for field distortions in real time. We employ a simultaneous localization and mapping technique to accurately estimate the pose of the tracked instrument while creating the field distortion map. We conducted experiments with six degrees-of-freedom motions in the presence of field distorting objects in research and clinical environments. We applied our approach to improve the EM tracking accuracy and compared our results to a conventional sensor fusion technique. Using our approach, the maximum tracking error was reduced by 67% for position measurements and by 64% for orientation measurements. Currently, clinical applications of EM trackers are hampered by the adverse distortion effects. Our approach introduces a novel method for dynamic field distortion compensation, independent from preoperative calibrations or external tracking devices, and enables reliable EM navigation for potential applications.

Description: A feasibility study on a new technique capable of monitoring localized sweat rate is explored in this paper. Wearable devices commonly used in clinical practice for sweat sampling (i.e., Macroducts) were positioned on the body of an athlete whose sweat rate was then monitored during cycling sessions. The position at which the sweat fills the Macroduct was indicated by a contrasting marker and captured via a series of time-stamped photos or a video recording of the device during an exercise period. Given that the time of each captured image/frame is known (either through time stamp on photos or the constant frame rate of the video capture), it was, therefore, possible to estimate the sweat flow rate through a simple calibration model. The importance of gathering such valuable information is described, together with the results from a number of exercise trials to investigate the viability of this approach.

Description: Objective: A novel high-precision approach [lifetime-decomposition measurement (LTDM)] for the assessment of the glomerular filtration rate (GFR) based on clearance measurements of exogenous filtration marker. Methods: The time-correlated single photon counting (TCSPC) acquisition in combination with a new decomposition method allows the separation of signal and background from transcutaneous measurements of GFR. Results: The performance of LTDM is compared versus the commercially available NIC-kidney patch-based system for transcutaneous GFR measurement. Measurements are performed in awake Sprague Dawley (SD) rats. Using the standard concentration required for the NIC-kidney system [7-mg/100-g body weight (b.w.) FITC-Sinistrin] as reference, the mean difference (bias) of the elimination curves GFR between LTDM and NIC-kidney was 4.8%. On the same animal and same day, the capability of LTDM to measure GFR with a FITC-Sinistrin dose reduced by a factor of 200 (35-μg/100-g b.w.) was tested as well. The mean differences (half lives with low dose using LTDM compared with those using first, the NIC-Kidney system and its standard concentration, and second, LTDM with the same concentration as for the NIC-Kidney system) were 3.4% and 4.5%, respectively. Conclusion: We demonstrate that with the LTDM strategy substantial reductions in marker concentrations are possible at the same level of accuracy. Significance: LTDM aims to resolve the issue of the currently necessary large doses of fluorescence tracer required for transcutaneous GFR measurement. Due to substantially less influences from autofluorescence and artifacts, the proposed method outperforms other existing techniques for accurate percutaneous organ function measurement.

Description: Classic brain–machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes.

Description: Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. Methods: Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e., atlases) were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. Permutation tests and indifference-zone ranking were performed to examine the statistical and practical significance, respectively. Results: The results suggest that DEEDS yielded the best registration performance. However, due to the overall low DSC values, and substantial portion of low-performing outliers, great care must be taken when image registration is used for local interpretation of abdominal CT. Conclusion: There is substantial room for improvement in image registration for abdominal CT. Significance: All data and source code are available so that innovations in registration can be directly compared with the current generation of tools without excessive duplication of effort.

Description: Goal : In K-edge tomographic imaging with photon counting detectors, the energy window width of photon counting detectors significantly affects the signal-to-noise ratio (SNR) of measured intensity data and the contrast-to-noise ratio (CNR) of reconstructed images. In this paper, we present an optimization method to determine an optimal window width around a K-edge for optimal SNR and CNR. Methods : An objective function is designed to describe SNR of the projection data based on the Poisson distribution of detected X-ray photons. Then, a univariate optimization method is applied to obtain an X-ray energy window width. Results : Numerical simulations are performed to evaluate the proposed method, and the results show that the optimal energy window width obtained from the proposed method produces not only optimal SNR data in the projection domain but also optimal CNR values in the image domain. Conclusion : The proposed method in the projection domain can determine an optimal energy window width for X-ray photon counting imaging, and achieve optimality in both projection and image domains. Significance : Our study provides a practical way to determine the optimal energy window width of photon counting detectors, which helps improve contrast resolution for X-ray K-edge tomographic imaging.

Description: Objective: The propagation of electrophysiological activity measured by multichannel devices could have significant clinical implications. Gastric slow waves normally propagate along longitudinal paths that are evident in recordings of serosal potentials and transcutaneous magnetic fields. We employed a realistic model of gastric slow wave activity to simulate the transabdominal magnetogastrogram (MGG) recorded in a multichannel biomagnetometer and to determine characteristics of electrophysiological propagation from MGG measurements. Methods: Using MGG simulations of slow wave sources in a realistic abdomen (both superficial and deep sources) and in a horizontally-layered volume conductor, we compared two analytic methods (second-order blind identification, SOBI and surface current density, SCD) that allow quantitative characterization of slow wave propagation. We also evaluated the performance of the methods with simulated experimental noise. The methods were also validated in an experimental animal model. Results: Mean square errors in position estimates were within 2 cm of the correct position, and average propagation velocities within 2 mm/s of the actual velocities. SOBI propagation analysis outperformed the SCD method for dipoles in the superficial and horizontal layer models with and without additive noise. The SCD method gave better estimates for deep sources, but did not handle additive noise as well as SOBI. Conclusion: SOBI-MGG and SCD-MGG were used to quantify slow wave propagation in a realistic abdomen model of gastric electrical activity. Significance: These methods could be generalized to any propagating electrophysiological activity detected by multichannel sensor arrays.

Description:   Goal : This study aims at a systematic assessment of five computational models of a birdcage coil for magnetic resonance imaging (MRI) with respect to accuracy and computational cost. Methods : The models were implemented using the same geometrical model and numerical algorithm, but different driving methods (i.e., coil “defeaturing”). The defeatured models were labeled as: specific ( S2 ), generic ( G32 , G16 ), and hybrid ( H16, $hbox{H16}_{{rm fr}text{-}{rm forced}}$ ). The accuracy of the models was evaluated using the “symmetric mean absolute percentage error” (“SMAPE”), by comparison with measurements in terms of frequency response, as well as electric ( $|{vec E}|$ ) and magnetic ( $| {vec B} |$ ) field magnitude. Results : All the models computed the $| {vec B} |$ within 35% of the measurements, only the S2 , G32, and H16 were able to accurately model the $|{vec E}|$ inside the phantom with a maximum SMAPE of 16%. Outside the phantom, only the S2 showed a SMAPE lower than 11%. Conclusions : Results showed that assessing the accuracy of $| {vec B} |$ based only on comparison along the central longitudinal line of the coil can be misleading. Generic or hybrid coils — when properly modeling the currents along the rings/rungs — were sufficient to accur- tely reproduce the fields inside a phantom while a specific model was needed to accurately model $|{vec E}|$ in the space between coil and phantom. Significance : Computational modeling of birdcage body coils is extensively used in the evaluation of radiofrequency-induced heating during MRI. Experimental validation of numerical models is needed to determine if a model is an accurate representation of a physical coil.

Description: Objective: The objective of this research was to develop a bioimpedance platform for monitoring fluid volume in residual limbs of people with trans-tibial limb loss using prostheses. Methods: A customized multifrequency current stimulus profile was sent to thin flat electrodes positioned on the thigh and distal residual limb. The applied current signal and sensed voltage signals from four pairs of electrodes located on the anterior and posterior surfaces were demodulated into resistive and reactive components. An established electrical model (Cole) and segmental limb geometry model were used to convert results to extracellular and intracellular fluid volumes. Bench tests and testing on amputee participants were conducted to optimize the stimulus profile and electrode design and layout. Results: The proximal current injection electrode needed to be at least 25 cm from the proximal voltage sensing electrode. A thin layer of hydrogel needed to be present during testing to ensure good electrical coupling. Using a burst duration of 2.0 ms, intermission interval of 100 μs, and sampling delay of 10 μs at each of 24 frequencies except 5 kHz, which required a 200-μs sampling delay, the system achieved a sampling rate of 19.7 Hz. Conclusion: The designed bioimpedance platform allowed system settings and electrode layouts and positions to be optimized for amputee limb fluid volume measurement. Significance: The system will be useful toward identifying and ranking prosthetic design features and participant characteristics that impact residual limb fluid volume.

Description: Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle–computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle–computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Description: We have been developing an automated cardiovascular drug infusion system for simultaneous control of arterial pressure (AP), cardiac output (CO), and left atrial pressure (P LA ) in decompensated heart failure (HF). In our prototype system, CO and P LA were measured invasively through thoracotomy. Furthermore, the control logic inevitably required use of inotropes to improve hemodynamics, which was not in line with clinical HF guidelines. The goal of this study was to solve these problems and develop a clinically feasible system. We integrated to the system minimally invasive monitors of CO and pulmonary capillary wedge pressure (PCWP, surrogates for P LA ) that we developed recently. We also redesigned the control logic to reduce the use of inotrope. We applied the newly developed system to nine dogs with decompensated HF. Once activated, our system started to control the infusion of vasodilator and diuretics in all the animals. Inotrope was not infused in three animals, and infused at minimal doses in six animals that were intolerant of vasodilator infusion alone. Within 50 min, our system controlled AP, CO, and PCWP to their respective targets accurately. Pulmonary artery catheterization confirmed optimization of hemodynamics (AP, from 98 ± 4 to 74 ± 11 mmHg; CO, from 2.2 ± 0.5 to 2.9 ± 0.3 L·min −1 ·m −2 ; PCWP, from 27.0 ± 6.6 to 13.8 ± 3.0 mmHg). In a minimally invasive setting while reducing the use of inotrope, our system succeeded in automatically optimizing the overall hemodynamics in canine models of HF. The present results pave the way for clinical application of our automated drug infusion system.

Description: Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid registration method based on our proposed accelerated-NSIFT and GMM registration-based parallel optimization (PO-GMMREG). Our method is accelerated by using the GPU/CUDA programming and preserving only the location information without constructing the descriptor of each interest point, while its robustness to missing correspondences and outliers is improved by converting the interest point matching to Gaussian mixture model alignment. The accuracy in the case of large pose differences is settled by our proposed PO-GMMREG algorithm by constructing a set of initial transformations. Experimental results demonstrate that our proposed algorithm can fast rigidly register 3-D medical images and is reliable for aligning 3-D scans even when they exhibit a poor initialization.

Description: Glaucoma is a neurological disorder leading to blindness initially through the loss of retinal ganglion cells, followed by loss of neurons higher in the visual system. Some work has been undertaken to develop prostheses for glaucoma patients targeting tissues along the visual pathway, including the lateral geniculate nucleus (LGN) of the thalamus, but especially the visual cortex. This review makes the case for a visual prosthesis that targets the LGN. The compact nature and orderly structure of this nucleus make it a potentially better target to restore vision than the visual cortex. Existing research for the development of a thalamic visual prosthesis will be discussed along with the gaps that need to be addressed before such a technology could be applied clinically, as well as the challenge posed by the loss of LGN neurons as glaucoma progresses.

Description: Alterations in the health of muscles can be evaluated through the use of electrical impedance myography (EIM). To date, however, nearly all work in this field has relied upon the measurement of muscle at rest. To provide an insight into the contractile mechanisms of healthy and disease muscle, we evaluated the alterations in the spectroscopic impedance behavior of muscle during the active process of muscle contraction. The gastrocnemii from a total of 13 mice were studied (five wild type, four muscular dystrophy animals, and four amyotrophic lateral sclerosis animals). Muscle contraction was induced via monophasic current pulse stimulation of the sciatic nerve. Simultaneously, multisine EIM (1 kHz to 1 MHz) and force measurements of the muscle were performed. Stimulation was applied at three different rates to produce mild, moderate, and strong contractions. We identified changes in both single and multifrequency data, as assessed by the Cole impedance model parameters. The processes of contraction and relaxation were clearly identified in the impedance spectra and quantified via derivative plots. Reductions in the center frequency ${f}_{mathrm{c}}$ were observed during the contraction consistent with the increasing muscle fiber diameter. Different EIM stimulation rate-dependencies were also detected across the three groups of animals.

Description: We describe a novel motion-tracking system, called MASK (magnetoarticulography for the assessment of speech kinematics) designed to track detailed orofacial movements during magnetoencephalographic (MEG) measures of human brain activity. A three-dimensional electromagnetic-tracking method was employed using lightweight coils energized with high-frequency sinusoidal currents, creating magnetic dipoles that can be continuously localized by the MEG sensors. In addition to being compatible with commercial MEG devices, this system has advantages over optical or video methods in that it can record nonline-of-sight movements (e.g., tongue movements) and advantages over surface electromyographic recordings, which are prone to movement-related artifacts and signal crosstalk. Static and dynamic tracking accuracy was evaluated using calibration devices with fixed intercoil distances. MEG data were collected in two healthy adult volunteers to test feasibility of tracking movements during tongue and facial movement, and during overt speech. The MASK system was shown to have sufficient static and dynamic accuracy to track orofacial movements within the MEG helmet. We successfully acquired spatially precise kinematic information time-locked to brain activity with high temporal resolution. We demonstrated successful tracking of oromotor and speech movements together with brain activity using the MASK system. This novel technology will provide an innovative tool in support of research and clinical applications for individuals with speech and other oromotor disorders.

Description: Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

Description: Embryo biopsies are routinely performed for preimplantation genetic diagnosis (PGD). In order to avoid blastomere membrane rupture and cell lysis, correct selection of a suitable dissection position on the zona pellucida (ZP) is necessary. Although, the technology for automated cell manipulation has advanced greatly over the past decade, fully automated embryo biopsy in PGD has not been realized yet. Automated PGD may ultimately set a new clinical standard that improves the consistency of outcomes, increases cell survival rates, flattens the learning curve of the manual procedure, and reduces the effects of human fatigue. In this paper, we present the first approach to automatically select a suitable ZP dissection position prior to embryo biopsy from a single focused embryo image based on edge detection. The proposed method consists of a technique that estimates the elliptical ZP boundaries and another two techniques that select the suitable position for ZP dissection. These techniques achieved success rates of 96%, 94%, and 94% respectively. In addition, the proposed ZP boundary estimation technique has the potential to perform ZP thickness variation (ZPTV) test and other ZP morphology measurements with further improvement in the future. Our methods provide a starting point for fast position selection prior to automatic embryo biopsy.

Description: Goal: The purpose of this study is to evaluate the usefulness of the boosting algorithm AdaBoost (AB) in the context of the sleep apnea-hypopnea syndrome (SAHS) diagnosis. Methods: We characterize SAHS in single-channel airflow (AF) signals from 317 subjects by the extraction of spectral and nonlinear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter to derive the optimum set of features among them. These are used to feed classifiers based on linear discriminant analysis (LDA) and classification and regression trees (CART). LDA and CART models are sequentially obtained through AB, which combines their performances to reach higher diagnostic ability than each of them separately. Results: Our AB-LDA and AB-CART approaches showed high diagnostic performance when determining SAHS and its severity. The assessment of different apnea-hypopnea index cutoffs using an independent test set derived into high accuracy: 86.5% (5 events/h), 86.5% (10 events/h), 81.0% (15 events/h), and 83.3% (30 events/h). These results widely outperformed those from logistic regression and a conventional event-detection algorithm applied to the same database. Conclusion: Our results suggest that AB applied to data from single-channel AF can be useful to determine SAHS and its severity. Significance: SAHS detection might be simplified through the only use of single-channel AF data.

Description: Objective: Lumpectomy, breast conserving tumor excision, is the standard surgical treatment in early stage breast cancer. A common problem with lumpectomy is that the tumor may not be completely excised, and additional surgery becomes necessary. We investigated if a surgical navigation system using intraoperative ultrasound improves the outcomes of lumpectomy and if such a system can be implemented in the clinical environment. Methods: Position sensors were applied on the tumor localization needle, the ultrasound probe, and the cautery, and 3-D navigation views were generated using real-time tracking information. The system was tested against standard wire-localization procedures on phantom breast models by eight surgical residents. Clinical safety and feasibility was tested in six palpable tumor patients undergoing lumpectomy by two experienced surgical oncologists. Results: Navigation resulted in significantly less tissue excised compared to control procedures (10.3 ± 4.4 versus 18.6 ± 8.7 g, p = 0.01) and lower number of tumor-positive margins (1/8 versus 4/8) in the phantom experiments. Excision-tumor distance was also more consistently outside the tumor margins with navigation in phantoms. The navigation system has been successfully integrated in an operating room, and user experience was rated positively by surgical oncologists. Conclusion: Electromagnetic navigation may improve the outcomes of lumpectomy by making the tumor excision more accurate. Significance: Breast cancer is the most common cancer in women, and lumpectomy is its first choice treatment. Therefore, the improvement of lumpectomy outcomes has a significant impact on a large patient population.

Description: Goal: The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods: An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion: The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance: Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.

Description:   Objective: This work aimed to find and evaluate a new method for detecting errors in continuous brain–computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. Methods: In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. Results: We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. Conclusion: Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. Significance: This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.

Description: Goal: Target-controlled infusion of anesthesia is a closed-loop automated drug delivery method with a computer-aided control. Our goal is to design and test an automated drug infusion platform for propofol delivery in total intravenous anesthesia (TIVA) administration. Methods: In the proposed method, a dilution chamber with first-order exponential decay characteristics was used to model the pharmacodynamics decay of a drug. The dilution chamber was connected to a flow system through an electrochemical cell containing an organic film-coated glassy carbon electrode as working electrode. To set up the feedback-controlled delivery platform and optimize its parameters, ferrocene methanol was used as a proxy of the propofol. The output signal of the sensor was connected to a PI controller, which prompted a syringe pump for feedback-controlled drug infusion. Results: The result is a bench-top drug infusion platform to automate the delivery of a propofol based on the measurement of concentration with an organic film-coated voltammetric sensor. Conclusion: To evaluate the performance characteristics of the infusion platform, the propofol concentration in the dilution chamber was monitored with the organic film-coated glassy carbon electrode and the difference between the set and measured concentrations was assessed. The feasibility of measurement-based feedback-controlled propofol delivery is demonstrated and confirmed. Significance: This platform will contribute to high-performance TIVA application of intravenous propofol anesthesia.

Description: Involuntary muscle activations are diagnostic indicators of neurodegenerative pathologies. Currently detected by invasive intramuscular electromyography, these muscle twitches are found to be visible in ultrasound images. We present an automated computational approach for the detection of muscle twitches, and apply this to two muscles in healthy and motor neuron disease-affected populations. The technique relies on motion tracking within ultrasound sequences, extracting local movement information from muscle. A statistical analysis is applied to classify the movement, either as noise or as more coherent movement indicative of a muscle twitch. The technique is compared to operator identified twitches, which are also assessed to ensure operator agreement. We find that, when two independent operators manually identified twitches, higher interoperator agreement (Cohen's $kappa$ ) occurs when more twitches are present ( $kappa = 0.94$ ), compared to a lower number ( $kappa = 0.49$ ). Finally, we demonstrate, via analysis of receiver operating characteristics, that our computational technique detects muscle twitches across the entire dataset with a high degree of accuracy ( $0.83〈$ accuracy $〈0.96$ ).

Description: Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.

Description: The accredited biomarker alpha-fetoprotein (AFP) offers limited sensitivity and specificity in the early detection of hepatocellular carcinoma (HCC). To improve the screening performance, des-gamma-carboxy prothrombin (DCP) has been identified as another promising biomarker of HCC, combined with AFP biomarkers. The results of the commercial optical enzyme-linked immunosorbent assay (ELISA) kit easily have the interference problem due to the optical methodology. The immunomagnetic reduction (IMR) assay based on the magnetic measurement was utilized to assay DCP biomarkers without the excellent antiinterference performances. A DCP magnetic reagent, composed of iron-oxide (Fe 3 O 4 ) magnetic nanoparticles coated with anti-DCP antibodies solved in phosphoryl-buffer solution, was synthesized and characterized. In the test of standard DCP antigens, superior antiinterference and sensitivity than optical ELISA were proved. In the animal test, the results indicate good agreement between the IMR assay findings and the tumor sizes of HCC rats at all time points after the HCC implantation. The feasibility of the developed DCP magnetic reagent with the IMR for the detection of DCP is verified, and demonstrates the high potential for future clinical applications.

Description: Goal: Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration. Method: The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution. Results: The method was tested on a database where 108 CGM signals were collected for 7 days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the “A” zone of the Clark error grid increased from 82.22% to 88.86%. Conclusion: The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration. Significance: The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Description: Goal: The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)—an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. Methods: Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts’ law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. Results: The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. Conclusion and Significance: Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

Description: Photoplethysmography (PPG) is a noninvasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of the conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

Description: Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis. As the first contribution, we present a shape motion model enabling an unsupervised decomposition of respiration induced high-dimensional body surface displacement fields into a low-dimensional representation encoding thoracic and abdominal breathing. Second, we propose a method designed for GPU architectures to quickly and robustly align our models to high-coverage multiview RI body surface data. With our fully automatic method, we obtain respiration surrogates yielding a Pearson correlation coefficient (PCC) of 0.98 with conventional surrogates based on manually selected regions on RI body surface data. Compared to impedance pneumography as a respiration signal that measures the change of lung volume, we obtain a PCC of 0.96. Using off-the-shelf hardware, our framework enables high temporal resolution respiration analysis at 50 Hz.

Description: Goal : This paper describes the development and testing of various position sensing systems (PSSs) for miniaturized long-term applications with a focus on their validation in a total artificial heart (TAH). After a short description of the TAH's functioning principle, the special requirements for the PSS resulting from the application in a TAH are investigated. Methods : Three PSS's were designed according to these requirements. A specially designed test method was used to first validate each PSS for general use in a miniaturized application. This test method validated the speed, resolution, and accuracy requirements for the PSS. In a second step, the PSS's were integrated in a TAH to measure its stroke position for the drive control. In this application, further requirements apart from miniaturization were considered. Each PSS's functionality in the TAH was validated in a mock circulation loop, which simulates the human circulatory system. Results : Two of the three designed PSS's showed satisfactory results for all tested requirements inside the pump, whereas the third PSS did not operate properly at full-pump capacity. The best performing PSS was chosen for further use in the TAH. It performed up to a beat rate of 220 b/m. Conclusion : The extensive validation resulted in an accurate, miniature PSS for a TAH. Significance : Besides the use in a TAH, the presented PSS's can be employed in a wide use of miniaturized applications. The introduced testing method allows the validation for general miniaturized applications, e.g., linear motor drives.

Description: Pulse trains are widely used in electroporation (EP) for both general biomedical research and clinical applications such as nonthermal tumor ablation. Here we use a computational method based on a meshed transport network to investigate a cell system model's response to a train of identical, evenly spaced electric field pulses. We obtain an unexpected result: the number of membrane pores decreases during the application of twenty 1.0 kV/cm, 100  $mu$ s pulses, delivered at 1 Hz. This pulse train initially creates 13,000 membrane pores, but pore number decreases by a factor of 15 to about 830 pores throughout subsequent pulses. We conclude that pore number can greatly diminish during a train of identical pulses, with direct consequences for the transport of solutes across an electroporated membrane. Although application of additional pulses is generally intended to increase the effects of EP, we show that these pulses do not significantly enhance calcium delivery into the cell. Instead, calcium delivery can be significantly increased by varying inter-pulse intervals. We show that inserting a 300-s interruption midway in a widely used eight-pulse train (a protocol for electrosensitization) yields a $sim$ twofold delivery increase. Overall, our modeling shows support for electrosensitization, in which multiple pulse protocols that maximize pore number over time can yield significant increase of transport of calcium compared to standard pulse trains.

Description: Goal: The purpose of this paper is to improve the detection of high-frequency sounds from the heart for better identification of turbulent blood flow in partially occluded coronary arteries. This paper also describes a method for the quantitative assessment of data quality. Methods: A very light-weight dual accelerometer has been developed that places a small mechanical load on the chest. When used in conjunction with a novel correlation-based analysis, this dual-signal transducer provides an estimate to the signal-to-noise ratio (SNR) of the acoustic signal. Results: The new transducer has significantly better SNR properties than the traditional cardiac microphones. This improvement is due to increased sensitivity to high-frequency signals not a reduction in noise and is likely the result of reduced mechanical loading on the chest. Conclusion: Substantial improvement in the detection of high-frequency heart sounds is possible as is quantitative assessment of data quality. Significance: The new transducer and analysis will lead to substantial improvements in the acoustic detection of partially occluded arteries associated with coronary artery disease. It is finally possible to obtain a measurement of the quality of heart sound signals as they are being recorded.

Description: The objective of this paper is to propose an asymmetric Gaussian function as an alternative to the existing active force-length models, and to optimize this model along with several other existing models by using the least squares curve fitting method. The minimal set of coefficients is identified for each of these models to facilitate the least squares curve fitting. Sarcomere simulated data and one set of rabbits extensor digitorum II experimental data are used to illustrate optimal curve fitting of the selected force–length functions. The results shows that all the curves fit reasonably well with the simulated and experimental data, while the Gordon–Huxley–Julian model and asymmetric Gaussian function are better than other functions in terms of statistical test scores root mean squared error and R-squared. However, the differences in RMSE scores are insignificant (0.3–6%) for simulated data and (0.2–5%) for experimental data. The proposed asymmetric Gaussian model and the method of parametrization of this and the other force–length models mentioned above can be used in the studies on active force–length relationships of skeletal muscles that generate forces to cause movements of human and animal bodies.

Description: This paper demonstrates flexible epineural strip electrodes (FLESE) for recording from small nerves. Small strip-shaped FLESE enables us to easily and closely stick on various sized nerves for less damage in a nerve and optimal recording quality. In addition, in order to enhance the neural interface, the gold electrode contacts were coated with carbon nanotubes, which reduced the impedance of the electrodes. We used the FLESEs to record electrically elicited nerve signals (compound neural action potentials) from the sciatic nerve in rats. Bipolar and differential bipolar configurations for the recording were investigated to optimize the recording configuration of the FLESEs. The successful results from differential bipolar recordings showed that the total length of FLESEs could be further reduced, maintaining the maximum recording ability, which would be beneficial for recording in very fine nerves. Our results demonstrate that new concept of FLESEs could play an important role in electroceuticals in near future.

Description: Objective : Spiral reentry is a recognized cause of tachycardia. Detection and tracking of the spiral core are essential for understanding the spiral wave dynamics. The core of the spiral corresponds to a phase singularity (PS), which can be identified in an optical mapping image by a kernel convolution method. However, because of a large number of false positives, this method cannot automatically and stably track the core of sustaining spiral reentry in optical mapping data. Method : We developed a new PS detection algorithm that quantifies the variance of phase values in a phase map and identifies the position of PS as its peak. Results : In comparison with the kernel convolution method, our method improved the precision of detecting a single sustaining spiral wave core from 73.1% to 99.8%. The precision of the proposed method for virtual-electrode-polarization-induced multiple PSs detections was also higher than the convolutional method. Conclusion : The proposed method detects PS by finding the peaks in the phase variance distribution of cardiac optical mapping image. It improved the precision of the core detection of the spiral wave in cardiac optical mapping images in comparison with the conventional kernel convolution method. Significance : The proposed method will reveal the spiral wave dynamics in optical mapping images better than existing approaches. The objective analysis method of a spiral wave is important for understanding the mechanisms and dynamics of serious heart arrhythmias.

Description: Objective : Photoplethysmography (PPG) is a noninvasive technique to measure the blood-volume pulse and derive various vital signs. Camera-based PPG imaging was recently proposed for clinical microvascular assessment, but motion robustness is still an issue for this technique. Our study aims to quantify cardiac-related, i.e., ballistocardiographic (BCG), motion as a source of artifacts in PPG imaging. Methods : In this paper, using the human head as a relevant region of interest, the amplitude of BCG-artifacts was modeled for a Lambertian surface illuminated by a light source. To derive peak-to-peak head displacements for the model, we recorded, on 54 subjects, PPG and inertial sensor data at the pulse and cranial vertex. We simulated the effect of light source location at a mesh representation of a human face and conducted additional experiments on a real subject. Results : Under nonorthogonal illumination, the relative strength of the BCG artifacts is strong enough, compared to the amplitude of PPG signals, to compromise PPG imaging in realistic scenarios. Particularly affected are the signals obtained in the nongreen part of the spectrum and/or when the incident angle at the skin surface exceeds 45 $^circ$ . Conclusion : From the model and an additional experiment conducted on real skin, we were able to prove that homogenous and orthogonal illumination is a means to minimize the problem. Significance : Our illumination recommendation provides a simple and effective means to improve the validity of remote PPG-imagers. We hope that it helps to prevent mistakes currently seen in many publications on remote PPG.

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: Objective: The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images. Methods: We propose a segmentation-guided partially-joint regression forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization method to extract high-level multiscale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts. Results: Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than $2$ mm. Conclusion: Our model has addressed challenges of both interpatient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization. Significance: Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency.

Description: In this paper, we propose a conceptually novel algorithm, namely “Spatial Subspace Rotation” (2SR), that improves the robustness of remote photoplethysmography. Based on the assumption of 1) spatially redundant pixel-sensors of a camera, and 2) a well-defined skin mask, our core idea is to estimate a spatial subspace of skin-pixels and measure its temporal rotation for pulse extraction, which does not require skin-tone or pulse-related priors in contrast to existing algorithms. The proposed algorithm is thoroughly assessed on a benchmark dataset containing 54 videos, which includes challenges of various skin-tones, body-motions in complex illuminance conditions, and pulse-rate recovery after exercise. The experimental results show that given a well-defined skin mask, 2SR outperforms the popular ICA-based approach and two state-of-the-art algorithms (CHROM and PBV). When comparing the pulse frequency spectrum, 2SR improves on average the SNR of ICA by 2.22 dB, CHROM by 1.56 dB, and PBV by 1.95 dB. When comparing the instant pulse-rate, 2SR improves on average the Pearson correlation and precision of ICA by 47% and 65%, CHROM by 22% and 23%, and PBV by 21% and 39%. ANOVA confirms the significant improvement of 2SR in peak-to-peak accuracy. The proposed 2SR algorithm is very simple to use and extend, i.e., the implementation only requires a few lines MATLAB code.

Description: Presents a listing of the handling editors for this issue of the publication.

Description: Objective: The atrioventricular (AV) node plays a central role in atrial fibrillation (AF), as it influences the conduction of impulses from the atria into the ventricles. In this paper, the statistical dual pathway AV node model, previously introduced by us, is modified so that it accounts for atrial impulse pathway switching even if the preceding impulse did not cause a ventricular activation. Methods: The proposed change in model structure implies that the number of model parameters subjected to maximum likelihood estimation is reduced from five to four. The model is evaluated using the data acquired in the RATe control in atrial fibrillation (RATAF) study, involving 24-h ECG recordings from 60 patients with permanent AF. Results: When fitting the models to the RATAF database, similar results were obtained for both the present and the previous model, with a median fit of $86%$ . The results show that the parameter estimates characterizing refractory period prolongation exhibit considerably lower variation when using the present model, a finding that may be ascribed to fewer model parameters. Conclusion: The new model maintains the capability to model RR intervals, while providing more reliable parameters estimates. Significance: The model parameters are expected to convey novel clinical information, and may be useful for predicting the effect of rate control drugs.

Description: Objective : Image registration of whole slide histology images allows the fusion of fine-grained information—like different immunohistochemical stains—from neighboring tissue slides. Traditionally, pathologists fuse this information by looking subsequently at one slide at a time. If the slides are digitized and accurately aligned at cell level, automatic analysis can be used to ease the pathologist's work. However, the size of those images exceeds the memory capacity of regular computers. Methods : We address the challenge to combine a global motion model that takes the physical cutting process of the tissue into account with image data that is not simultaneously globally available. Typical approaches either reduce the amount of data to be processed or partition the data into smaller chunks to be processed separately. Our novel method first registers the complete images on a low resolution with a nonlinear deformation model and later refines this result on patches by using a second nonlinear registration on each patch. Finally, the deformations computed on all patches are combined by interpolation to form one globally smooth nonlinear deformation. The NGF distance measure is used to handle multistain images. Results : The method is applied to ten whole slide image pairs of human lung cancer data. The alignment of 85 corresponding structures is measured by comparing manual segmentations from neighboring slides. Their offset improves significantly, by at least 15%, compared to the low-resolution nonlinear registration. Conclusion/Significance : The proposed method significantly improves the accuracy of multistain registration which allows us to compare different antibodies at cell level.

Description: Barrett's oesophagus, a premalignant condition of the oesophagus has been on a rise in the recent years. The standard diagnostic protocol for Barrett's involves obtaining biopsies at suspicious regions along the oesophagus. The localization and tracking of these biopsy sites “interoperatively” poses a significant challenge for providing targeted treatments and tracking disease progression. This paper proposes an approach to provide guided navigation and relocalization of the biopsy sites using an electromagnetic tracking system. The characteristic of our approach over existing ones is the integration of an electromagnetic sensor at the flexible endoscope tip, so that the endoscopic camera depth inside the oesophagus can be computed in real time, allowing to retrieve and display an image from a previous exploration at the same depth. We first describe our system setup and methodology for interoperative registration. We then propose three incremental experiments of our approach. First, on synthetic data with realistic noise model to analyze the error bounds of our system. The second on in vivo pig data using an optical tracking system to provide a pseudo ground truth. Accuracy results obtained were consistent with the synthetic experiments despite uncertainty introduced due to breathing motion, and remain inside acceptable error margin according to medical experts. Finally, a third experiment designed using data from pigs to simulate a real task of biopsy site relocalization, and evaluated by ten gastro-intestinal experts. It clearly demonstrated the benefit of our system toward assisted guidance by improving the biopsy site retrieval rate from 47.5% to 94%.

Description: Diffuse optical tomography is an imaging technique, based on evaluation of how light propagates within the human head to obtain the functional information about the brain. Precision in reconstructing such an optical properties map is highly affected by the accuracy of the light propagation model implemented, which needs to take into account the presence of clear and scattering tissues. We present a numerical solver based on the radiosity-diffusion model, integrating the anatomical information provided by a structural MRI. The solver is designed to run on parallel heterogeneous platforms based on multiple GPUs and CPUs. We demonstrate how the solver provides a 7 times speed-up over an isotropic-scattered parallel Monte Carlo engine based on a radiative transport equation for a domain composed of 2 million voxels, along with a significant improvement in accuracy. The speed-up greatly increases for larger domains, allowing us to compute the light distribution of a full human head ( $approx$ 3 million voxels) in 116 s for the platform used.

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Description: Objective : To develop a compact probe that can be used to monitor humidity in ventilator care equipment. A mesoporous film of alternate layers of Poly(allylamine hydrochloride) (PAH) and silica (SiO2) nanoparticles (bilayers), deposited onto an optical fibre was used. The sensing film behaves as a Fabry-Perot cavity of low-finesse where the absorption of water vapour changes the optical thickness and produces a change in reflection proportional to humidity. Methods : The mesoporous film was deposited upon the cleaved tip of an optical fibre using the layer-by-layer method. The sensor was calibrated in a bench model against a commercially available capacitive sensor. The sensitivity and response time were assessed in the range from 5 % relative humidity (RH) to 95%RH for different numbers of bilayers up to a maximum of nine. Results : The sensitivity increases with the number of bilayers deposited; sensitivity of 2.28 mV/%RH was obtained for nine bilayers. The time constant of the response was 1.13 s ± 0.30 s which is faster than the commercial device (measured as 158 s). After calibration, the optical fibre humidity sensor was utilised in a bench top study employing a mechanical ventilator. The fast response time enabled changes in humidity in individual breaths to be resolved. Conclusion : Optical fibre sensors have the potential to be used to monitor breath to breath humidity during ventilator care. Significance : Control of humidity is an essential part of critical respiratory care and the developed sensor provides a sensitive, compact and fast method of humidity monitoring.

Description: Goals: Transcranial magnetic stimulation (TMS) is increasingly used as a diagnostic and therapeutic tool for numerous neuropsychiatric disorders. The use of TMS might cause whole-body exposure to undesired induced currents in patients and TMS operators. The aim of this study is to test and justify a simple analytical model known previously, which may be helpful as an upper estimate of eddy current density at a particular distant observation point for any body composition and any coil setup. Methods: We compare the analytical solution with comprehensive adaptive mesh refinement-based FEM simulations of a detailed full-body human model, two coil types, five coil positions, about 100 000 observation points, and two distinct pulse rise times; thus, providing a representative number of different datasets for comparison, while also using other numerical data. Results: Our simulations reveal that, after a certain modification, the analytical model provides an upper estimate for the eddy current density at any location within the body. In particular, it overestimates the peak eddy currents at distant locations from a TMS coil by a factor of 10 on average. Conclusion: The simple analytical model tested in this study may be valuable as a rapid method to safely estimate levels of TMS currents at different locations within a human body. Significance: At present, safe limits of general exposure to TMS electric and magnetic fields are an open subject, including fetal exposure for pregnant women.

Description: Endobronchial ultrasound (EBUS) is now commonly used for cancer-staging bronchoscopy. Unfortunately, EBUS is challenging to use and interpreting EBUS video sequences is difficult. Other ultrasound imaging domains, hampered by related difficulties, have benefited from computer-based image-segmentation methods. Yet, so far, no such methods have been proposed for EBUS. We propose image-segmentation methods for 2-D EBUS frames and 3-D EBUS sequences. Our 2-D method adapts the fast-marching level-set process, anisotropic diffusion, and region growing to the problem of segmenting 2-D EBUS frames. Our 3-D method builds upon the 2-D method while also incorporating the geodesic level-set process for segmenting EBUS sequences. Tests with lung-cancer patient data showed that the methods ran fully automatically for nearly 80% of test cases. For the remaining cases, the only user-interaction required was the selection of a seed point. When compared to ground-truth segmentations, the 2-D method achieved an overall Dice index = 90.0% $pm$ 4.9%, while the 3-D method achieved an overall Dice index = 83.9 $pm$ 6.0%. In addition, the computation time (2-D, 0.070 s/frame; 3-D, 0.088 s/frame) was two orders of magnitude faster than interactive contour definition. Finally, we demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.

Description: A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher–Reeves’ conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from $88%$ to $94%$ , the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as $94%$ indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

Description: Three-dimensional (3-D) reconstruction of the coronary artery is important for the diagnosis and interventional treatment of cardiovascular diseases. In this paper, a novel mean composited external force back-projective composition model is proposed and integrated into the deformable model framework for the 3-D reconstruction of coronary arteries from multiple angiograms. The parametric snake evolves toward the real vascular centerline in 3-D space based on the integrated internal energy and composited external energy. In addition, a polynomial function is constructed to determine the diameter of the cross section of the vascular segments, which fully utilizes the back-projection information of multiple angiograms. The deformable and proposed methods are comparatively validated using phantom datasets and routine angiographic images with respect to space and reprojection Euclidean distance errors. The experimental results demonstrate the effectiveness and robustness of the proposed model, which can achieve a mean space error of 0.570 mm and a mean reprojection error of 0.351 mm. In addition, the influence of the angle difference to the reconstruction accuracy is discussed and validated on phantom datasets, which demonstrate that an angle difference of for any two angiograms is suitable for the 3-D reconstruction process.

Description: Objective: Measurements of ultrawideband (UWB) pulses reflected by the human body are conducted to evidence the differences in the received signal time behaviors due to respiration phases, and to experimentally verify previously obtained numerical results on the body's organs responsible for pulse reflection. Methods: Two experimental setups are used. The first one is based on a commercially available impulse radar system integrated on a single chip, while the second one implements an indirect time-domain reflectometry technique using a vector network analyzer controlled by a LabVIEW virtual instrument running on a laptop. Results: When the UWB source is placed close to the human body, a small reflection due to the lung boundaries is present in the received pulse well distanced in time from the reflection due to the air–skin interface; this reflection proved to be linked to the different respiration phases. Conclusions: The changes in the reflected pulse could be used to detect, through wearable radar systems, lung movements associated with the breath activity. Significance: The development of a wearable radar system is of great importance because it allows the breath activity sensing without interfering with the subject daily activities.

Description: Objective: Achieving better surgical outcomes in cases of traumatic bone fractures requires postoperative monitoring of changes in the growth and mechanical properties of the tissue and bones during the healing process. While current in-vivo imaging techniques can provide a snapshot of the extent of bone growth, it is unable to provide a history of the healing process, which is important if any corrective surgery is required. Monitoring the time evolution of in-vivo mechanical loads using existing technology is a challenge due to the need for continuous power while maintaining patient mobility and comfort. Methods: This paper investigates the feasibility of self-powered monitoring of the bone-healing process using our previously reported piezo-floating-gate (PFG) sensors. The sensors are directly integrated with a fixation device and operate by harvesting energy from microscale strain variations in the fixation structure. Results: We show that the sensors can record and store the statistics of the strain evolution during the healing process for offline retrieval and analysis. Additionally, we present measurement results using a biomechanical phantom comprising of a femur fracture fixation plate; bone healing is emulated by inserting different materials, with gradually increasing elastic moduli, inside a fracture gap. Conclusion: The PFG sensor can effectively sense, compute, and record continuously evolving statistics of mechanical loading over a typical healing period of a bone, and the statistics could be used to differentiate between different bone-healing conditions. Significance: The proposed sensor presents a reliable objective technique to assess bone-healing progress and help decide on the removal time of the fixation device.

Description: Goal: Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3] . Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. Results: An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4] . Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%–14%, and model uncertainties and disturbances. Conclusion: The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven ( a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. Significance: This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization.

Description: Goal: The aim of this work is to produce a consistent set of six conductivity values for use in the bidomain model of cardiac tissue. Methods: Studies in 2007 by Hooks et al. and in 2009 by Caldwell et al. have found that, in the directions longitudinal:transverse:normal (l:t:n) to the cardiac fibers, ratios of bulk conductivities and conduction velocities are each approximately in the ratio 4:2:1. These results are used here as the basis for a method that can find sets of six normalized bidomain conductivity values. Results: It is found that the ratios involving transverse and normal conductivities are quite consistent, allowing new light to be shed on conductivity in the normal direction. For example, it is found that the ratio of transverse to normal conductivity is much greater in the intracellular (i) than the extracellular (e) domain. Using parameter values from experimental studies leads to the proposal of a new nominal six conductivity dataset: $g_{il}=2.4, g_{el}=2.4, g_{it}=0.35, g_{et}=2.0, g_{{in}}=0.08$ , and $g_{en}=1.1$ (all in mS/cm). Conclusion: When it is used to model partial thickness ischaemia, this dataset produces epicardial potential distributions in accord with experimental studies in an animal model. It is, therefore, suggested that the dataset is suitable for use in numerical simulations. Significance: Since the bidomain approach is the most commonly used method for modeling cardiac electrophysiological phenomena, new information about conductivity in the normal direction, as well as a consistent set of six conductivity values, is valuable for researchers who perform simulation studies.

Description: Goal: The objective of this study is to design and develop a portable tool consisting of a disposable biochip for measuring electrothermomechanical (ETM) properties of breast tissue. Methods: A biochip integrated with a microheater, force sensors, and electrical sensors is fabricated using microtechnology. The sensor covers the area of 2 mm and the biochip is 10 mm in diameter. A portable tool capable of holding tissue and biochip is fabricated using 3-D printing. Invasive ductal carcinoma and normal tissue blocks are selected from cancer tissue bank in Biospecimen Repository Service at Rutgers Cancer Institute of New Jersey. The ETM properties of the normal and cancerous breast tissues (3-mm thickness and 2-mm diameter) are measured by indenting the tissue placed on the biochip integrated inside the 3-D printed tool. Results: Integrating microengineered biochip and 3-D printing, we have developed a portable cancer diagnosis device. Using this device, we have shown a statistically significant difference between cancerous and normal breast tissues in mechanical stiffness, electrical resistivity, and thermal conductivity. Conclusion: The developed cancer diagnosis device is capable of simultaneous ETM measurements of breast tissue specimens and can be a potential candidate for delineating normal and cancerous breast tissue cores. Significance: The portable cancer diagnosis tool could potentially provide a deterministic and quantitative information about the breast tissue characteristics, as well as the onset and disease progression of the tissues. The tool can be potentially used for other tissue-related cancers.

Description: Background: Total liquid ventilation (TLV) consists in filling the lungs with a perfluorocarbon (PFC) and using a liquid ventilator to ensure a tidal volume of oxygenated, CO $_2$ -free and temperature-controlled PFC. Having a much higher thermal capacity than air, liquid PFCs assume that the filled lungs become an efficient heat exchanger with pulmonary circulation. Objective:  The objective of the present study was the development and validation of a parametric lumped thermal model of a subject in TLV. Methods:  The lungs were modeled as one compartment in which the control volume varied as a function of the tidal volume. The heat transfer in the body was modeled as seven parallel compartments representing organs and tissues. The thermal model of the lungs and body was validated with two groups of lambs of different ages and weights (newborn and juvenile) undergoing an ultrafast mild therapeutic hypothermia induction by TLV. Results: The model error on all animals yielded a small mean error of $mathbf {-0.1 pm 0.4}$   $^circ$ C for the femoral artery and $mathbf {0.0 pm 0.1 }$   $^circ$ C for the pulmonary artery. Conclusion:  The resulting experimental validation attests that the model provided an accurate estimation of the systemic arterial temperature and the venous return temperature. Significance:  This comprehensive thermal model of the lungs and body has the advantage of closely modeling the rapid thermal dynamics in TLV. The model can explain how the time to achieve mild hypo- hermia between newborn and juvenile lambs remained similar despite of highly different physiological and ventilatory parameters. The strength of the model is its strong relationship with the physiological parameters of the subjects, which suggests its suitability for projection to humans.

Description: Goal: The goal of this paper is to propose a model-based control design framework, adapted to the development of control modules for medical devices. A particular example is presented in which instantaneous heart rate is regulated in real-time, by modulating, in an adaptive manner, the current delivered to the vagus nerve by a neuromodulator. Methods: The proposed framework couples a control module, based on a classical PI controller, a mathematical model of the medical device, and a physiological model representing the cardiovascular responses to vagus nerve stimulation (VNS). In order to analyze and evaluate the behavior of the device, different control parameters are tested on a “virtual population,” generated with the model, according to the Latin Hypercube sampling method. In particular, sensitivity analyses are applied for the identification of a domain of interest in the space of the control parameters. The obtained control parameter domain has been validated in an experimental evaluation on six sheep. Results: A range of control parameters leading to accurate results was successfully estimated by the proposed model-based design method. Experimental evaluation of the control parameters inside such a domain led to the best compromise between accuracy and time response of the VNS control. Conclusion: The feasibility and usefulness of the proposed model-based design method were shown, leading to a functional, real-time closed-loop control of the VNS for the regulation of heart rate.

Description: Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.

Description: Objective: The aim of this study was to investigate the feasibility of using an inductive tongue control system (ITCS) for controlling robotic/prosthetic hands and arms. Methods: This study presents a novel dual modal control scheme for multigrasp robotic hands combining standard electromyogram (EMG) with the ITCS. The performance of the ITCS control scheme was evaluated in a comparative study. Ten healthy subjects used both the ITCS control scheme and a conventional EMG control scheme to complete grasping exercises with the IH1 Azzurra robotic hand implementing five grasps. Time to activate a desired function or grasp was used as the performance metric. Results: Statistically significant differences were found when comparing the performance of the two control schemes. On average, the ITCS control scheme was 1.15 s faster than the EMG control scheme, corresponding to a 35.4% reduction in the activation time. The largest difference was for grasp 5 with a mean AT reduction of 45.3% (2.38 s). Conclusion: The findings indicate that using the ITCS control scheme could allow for faster activation of specific grasps or functions compared with a conventional EMG control scheme. Significance: For transhumeral and especially bilateral amputees, the ITCS control scheme could have a significant impact on the prosthesis control. In addition, the ITCS would provide bilateral amputees with the additional advantage of environmental and computer control for which the ITCS was originally developed.

Description: Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database . The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.

Description: Objective: Deep transcranial magnetic stimulation (dTMS) has been recently used in several clinical studies as diagnostic and therapeutic tool. However, electric field ( E ) distributions induced in the brain by dTMS are still unknown. This paper provides a characterization of the induced E distributions in the brain of a realistic human model due to 16 different coil configurations. Methods: The scalar potential finite-element method was used to calculate the E distributions differentiating the brain structures, e.g., cortex, white matter, anterior cingulated cortex, cerebellum, thalamus, hypothalamus, nucleus accumbens, amygdale, and hippocampus. Results: Our results support that the double cone coils and the large diameter circular coils are more prone to activate deeper brain structures but are also characterized by a reduced focality on the surface of the cortex, with the consequent possible counter effect of stimulating regions not of interest. The Hesed coils, although their ability to reach deep brain tissues is lower, seem to be more able to reduce the effect on other brain regions where the stimulation is undesired. Conclusion: All the coil configurations resulted subjected to a depth-focality tradeoff. Significance: Since there is not a configuration that is capable of achieving a stimulation both deep and focal, the selection of the most suitable coil settings for a specific clinical application should be based on a balanced evaluation between these two different needs.

Description: Goal: To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions. Methods: This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13–14, 2014. Results: The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies. Conclusions: The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology. Significance: The development of new neurotechnologies (e.g., bidirectional brain–computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

Description: Objective: QRS detection algorithms are needed to analyze electrocardiogram (ECG) recordings generated in telehealth environments. However, the numerous published QRS detectors focus on clean clinical data. Here, a “UNSW” QRS detection algorithm is described that is suitable for clinical ECG and also poorer quality telehealth ECG. Methods: The UNSW algorithm generates a feature signal containing information about ECG amplitude and derivative, which is filtered according to its frequency content and an adaptive threshold is applied. The algorithm was tested on clinical and telehealth ECG and the QRS detection performance is compared to the Pan–Tompkins (PT) and Gutiérrez–Rivas (GR) algorithm. Results: For the MIT-BIH Arrhythmia database (virtually artifact free, clinical ECG), the overall sensitivity ( Se ) and positive predictivity (+ P ) of the UNSW algorithm was >99%, which was comparable to PT and GR. When applied to the MIT-BIH noise stress test database (clinical ECG with added calibrated noise) after artifact masking, all three algorithms had overall Se >99%, and the UNSW algorithm had higher + P (98%, p 〈 0.05) than PT and GR. For 250 telehealth ECG records (unsupervised recordings; dry metal electrodes), the UNSW algorithm had 98% Se and 95% + P which was superior to PT (+ P : p 〈 0.001) and GR ( Se and + P : p 〈 0.001). Conclusion: This is the first study to describe a QRS detection algorithm for telehealth data and evaluate it on clinical and telehealth ECG with superior results to published algorithms. Significance: The UNSW algorithm could be used to mana- e increasing telehealth ECG analysis workloads.

Description: Goal: Time–frequency analysis incorporating the wavelet transform followed by the principal component analysis (WT-PCA) has been a powerful approach for the analysis of biomedical signals, such as electromyography (EMG), electroencephalography, electrocardiography, and Doppler ultrasound. Time–frequency coefficients at various scales were usually transformed into a 1-D array using only a single or a few signal channels. The steady improvement of biomedical recording techniques has increasingly permitted the registration of a high number of channels. However, WT-PCA is not applicable to high-dimensional recordings due to the curse of dimensionality and small sample size problem. In this study, we present a multiscale two-directional 2-D principal component analysis method for the efficient and effective extraction of essential feature information from high-dimensional signals. Multiscale matrices constructed in the first step incorporate the spatial correlation and physiological characteristics of subband signals among channels. In the second step, the two-directional 2-D PCA operates on the multiscale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify 20 hand movements using 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for a high-dimensional biomedical signal analysis.

Description: Objective : We address the problem of characterization of afterdischarges (ADs) that often arise in patients with intractable focal epilepsy who, as part of their evaluation, undergo cortical electrical stimulation: A standard diagnostic and evaluation procedure before respective surgery. Results : A total of 1333 channels of data recorded in 17 trials of seven patients whose EEG showed ADs (on a total of 156 channels) during cortical stimulation were examined in the time-scale domain using a complex Morlet scalogram. We found excellent characterization of the AD channels based on the distribution functions of the sum of the wavelet coefficients in the two lowest scales corresponding to the frequency range [20, 80] Hz, i.e., the $beta$ and $gamma$ ranges of EEG. Conclusion : We suggest that the transient Morlet wavelet and the scale domain activity function of the EEG in the two lowest scales (as defined in this paper) could serve as a very useful decision aid in the identification of ADs during and after cortical electrical stimulation. Significance : In patients undergoing cortical electrical stimulation, AD waveforms can cause misleading test results by altering the ongoing electroencephalogram (EEG), and can become unwanted seizures. Any process to suppress the ADs rests on a reliable method to distinguish them from normal EEG channels, a task that is usually performed by visual inspection, and that is complicated by the fact that ADs have multiple distinct morphologies. The single feature of the EEG in our study resulted in average probability of detection of $0.99$ with an average false alarm probability of $0.04$ . It is likely that the addition of one or two more features to our decision aid could improve sensitivity and selectivity to near perfection.

Description: Objective: Acoustic radiation force (ARF)-based approaches to measure tissue elasticity require transmission of a focused high-energy acoustic pulse from a stationary ultrasound probe and ultrasound-based tracking of the resulting tissue displacements to obtain stiffness images or shear wave speed estimates. The method has established benefits in biomedical applications such as tumor detection and tissue fibrosis staging. One limitation, however, is the dependence on applied probe pressure, which is difficult to control manually and prohibits standardization of quantitative measurements. To overcome this limitation, we built a robot prototype that controls probe contact forces for shear wave speed quantification. Methods: The robot was evaluated with controlled force increments applied to a tissue-mimicking phantom and in vivo abdominal tissue from three human volunteers. Results: The root-mean-square error between the desired and measured forces was 0.07 N in the phantom and higher for the fatty layer of in vivo abdominal tissue. The mean shear wave speeds increased from 3.7 to 4.5 m/s in the phantom and 1.0 to 3.0 m/s in the in vivo fat for compressive forces ranging from 2.5 to 30 N. The standard deviation of shear wave speeds obtained with the robotic approach were low in most cases ( $〈$ 0.2 m/s) and comparable to that obtained with a semiquantitative landmark-based method. Conclusion: Results are promising for the introduction of robotic systems to control the applied probe pressure for ARF-based measurements of tissue elasticity. Significance: This approach has potential benefits in longitudinal studies of disease progression, comparative studies between patients, and large-scale multidimensional elasticity imaging.

Description: Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis. In this study, we propose a novel OSA detection approach based on ECG signals by considering temporal dependence within segmented signals. A discriminative hidden Markov model (HMM) and corresponding parameter estimation algorithms are provided. In addition, subject-specific transition probabilities within the model are employed to characterize the subject-to-subject differences of potential OSA patients. To validate our approach, 70 recordings obtained from the Physionet Apnea-ECG database were used. Accuracies of 97.1% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. Compared with other existing methods that simply ignore the temporal dependence of signals, the proposed HMM-based detection approach delivers more satisfactory detection performance and could be extended to other disease diagnosis applications.

Description: This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. Methods : the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. Objective : NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. Results : This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). Significance : more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. Conclusion : this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

Description: Goal: The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone-beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane, determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. Methods: The solution is an integration of three-dimensional (3-D) weighted axial CB-BPF/DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting the reconstruction accuracy, and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate the performance of the proposed algorithm. Results: Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3-D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Conclusion: Integrated with orthogonal butterfly filtering, the 3-D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3-D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. Significance: The proposed 3-D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications.

Description: We present a new gaze tracking-based navigation scheme for proton beam radiation of intraocular tumors and we show the technical integration into the treatment facility. Currently, to treat a patient with such a tumor, a medical physicist positions the patient and the affected eye ball such that the radiation beam targets the tumor. This iterative eye positioning mechanism requires multiple X-rays, and radio-opaque clips previously sutured on the target eyeball. We investigate a possibility to replace this procedure with a noninvasive approach using a 3-D model-based gaze tracker. Previous work does not cover a comparably extensive integration of a gaze tracking device into a state-of-the-art proton beam facility without using additional hardware, such as a stereo optical tracking system. The integration is difficult because of limited available physical space, but only this enables to quantify the overall accuracy. We built a compact gaze tracker and integrated it into the proton beam radiation facility of the Paul Scherrer Institute in Villigen, Switzerland. Our results show that we can accurately estimate a healthy volunteer's point of gaze, which is the basis for the determination of the desired initial eye position. The proposed method is the first crucial step in order to make the proton therapy of the eye completely noninvasive.

Description: Goal: Quantitative assessment of hepatic insulin extraction (HE) after an oral glucose challenge, e.g., a meal, is important to understand the regulation of carbohydrate metabolism. The aim of the current study is to develop a model of system for estimating HE. Methods : Nine different models, of increasing complexity, were tested on data of 204 normal subjects, who underwent a mixed meal tolerance test, with frequent measurement of plasma glucose, insulin, and C-peptide concentrations. All these models included a two-compartment model of C-peptide kinetics, an insulin secretion model, a compartmental model of insulin kinetics (with number of compartments ranging from one to three), and different HE descriptions, depending on plasma glucose and insulin. Model performances were compared on the basis of data fit, precision of parameter estimates, and parsimony criteria. Results : The three-compartment model of insulin kinetics, coupled with HE depending on glucose concentration, showed the best fit and a good ability to precisely estimate the parameters. In addition, the model calculates basal and total indices of HE ( ${rm HE_{b}}$ and ${rm HE_{tot}}$ , respectively), and provides an index of HE sensitivity to glucose ( ${rm S}_G^{rm HE}$ ). Conclusion : A new physiologically based HE model has been developed, which allows an improved quantitative description of glucose regulation. Significance : The use of the new model provides an in-depth description of insulin kinetics, thus enabling a better understanding of a given subject's metabolic state.

Description: Objective: Improve the reconstructed image with fast and multiclass dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to provide adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multiclass dictionaries is proposed and solved using a fast alternating direction method of multipliers. Results: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. Conclusion: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction. Significance: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality.

Description: Objective: Cortical source imaging aims at identifying activated cortical areas on the surface of the cortex from the raw electroencephalogram (EEG) data. This problem is ill posed, the number of channels being very low compared to the number of possible source positions. Methods: In some realistic physiological situations, the active areas are sparse in space and of short time durations, and the amount of spatio-temporal data to carry the inversion is then limited. In this study, we propose an original data driven space-time-frequency (STF) dictionary which takes into account simultaneously both spatial and time-frequency sparseness while preserving smoothness in the time frequency (i.e., nonstationary smooth time courses in sparse locations). Based on these assumptions, we take benefit of the matching pursuit (MP) framework for selecting the most relevant atoms in this highly redundant dictionary. Results: We apply two recent MP algorithms, single best replacement (SBR) and source deflated matching pursuit, and we compare the results using a spatial dictionary and the proposed STF dictionary to demonstrate the improvements of our multidimensional approach. We also provide comparison using well-established inversion methods, FOCUSS and RAP-MUSIC, analyzing performances under different degrees of nonstationarity and signal to noise ratio. Conclusion: Our STF dictionary combined with the SBR approach provides robust performances on realistic simulations. From a computational point of view, the algorithm is embedded in the wavelet domain, ensuring high efficiency in term of computation time. Significance: The proposed approach ensures fast and accurate sparse cortical localizations on highly nonstationary and noisy data.

Description: Objective: To evaluate the recently proposed nonlinear difference imaging approach to electrical impedance tomography (EIT) in realistic 3-D geometries. Methods: In this paper, the feasibility of nonlinear difference approach-based EIT is tested using simulation studies in 3-D geometries of thorax and larynx, and with an experimental study of a thorax-shaped water tank. All test cases exhibit severe modeling errors due to uncertainty in the boundary shape of the body. Results: In all test cases, the conductivity change reconstructed with nonlinear difference imaging outperforms the conventional reconstructions qualitatively and quantitatively. Conclusion: The results demonstrate that the nonlinear difference reconstructions tolerate geometrical modeling errors at least to the same extent as the conventional linear approach and produce quantitatively more accurate information on the conductivity change. Significance: Physiological processes that produce changes in the electrical conductivity of the body can be monitored with difference imaging based on EIT. The wide popularity of linearized difference imaging in EIT is mainly based on its good tolerance for the ubiquitous modeling errors, which are predominantly caused by inexact knowledge of the body geometry. However, the linearized difference imaging produces only qualitative information on the conductivity change, and the feasibility of the estimates also depends on the selection of the linearization point which ideally should be equal to the conductivity of the initial state. Based on the findings of this paper, these problems can be avoided by nonlinear difference imaging, and potentially the approach can enable quantitative imaging of conductivity change in medical applications.

Description: Human body communication (HBC) provides a promising physical layer for wireless body area networks (BANs) in healthcare and medical applications, because of its low propagation loss and high security characteristics. In this study, we have developed a wearable electrocardiogram (ECG) which employs impulse radio (IR)-type HBC technology for transmitting vital signals on the human body in a wearable BAN scenario. The HBC-based wearable ECG has two excellent features. First, the wideband performance of the IR scheme contributed to very low radiation power so that the transceiver is easy to satisfy the extremely weak radio laws, which does not need a license. This feature can provide big convenience in the use and spread of the wearable ECG. Second, the realization of common use of sensing and transmitting electrodes based on time sharing and capacitive coupling largely simplified the HBC-based ECG structure and contributed to its miniaturization. To verify the validity of the HBC-based ECG, we evaluated its communication performance and ECG acquisition performance. The measured bit error rate, smaller than 10 $^{-3}$ at 1.25 Mb/s, showed a good physical layer communication performance, and the acquired ECG waveform and various heart-rate variability parameters in time and frequency domains exhibited good agreement with a commercially available radio-frequency ECG and a Holter ECG. These results sufficiently showed the validity and feasibility of the HBC-based ECG for healthcare applications. This should be the first time to have realized a real-time ECG transmission by using the HBC technology.

Description: Objective: Airway resistance is the mechanical cause of most of the symptoms in obstructive pulmonary disease, and can be considered as the primary measure of disease severity. A low-cost and noninvasive method to measure the airway resistance that does not require patient effort could be of great benefit in evaluating the severity of lung diseases, especially in patient population that are unable to use spirometry, such as young children. Methods: The Vision-Based Passive Airway Resistance Estimation (VB-PARE) technology is a passive method to measure airway resistance noninvasively. The airway resistance is estimated from: 1) airflow extracted from processing depth data captured by a Microsoft Kinect, and 2) Pulsus Paradoxus extracted from a pulse oximeter (SpO $_2$ ). Results: To verify the validity and accuracy of the VB-PARE, two phases of experiment were conducted. In Phase I, spontaneous breathing data was collected from 14 healthy participants with externally induced airway obstruction, and the accuracy of $76.2pm 13.8%$ was achieved in predicting three levels of obstruction severity. In Phase II, VB-PARE outputs were compared with the clinical results from 14 patients. VB-PARE estimated the tidal volume with an average error of $0.07pm 0.06$ liter. Also, patients with airway obstruction were detected with $80%$ accuracy. Conclusion: Using the information extracted from Kinect and SpO $_2$ , here, we present a quantitative method to measure the severity of airway obstruction without requiring active patient involvement. Significanc- : The proposed VB-PARE system contributes to the state-of-art respiration monitoring methods by expanding the idea of passive and noninvasive airway resistance measurement.

Description: Objective: Transcranial focused ultrasound (tFUS) has been introduced as a noninvasive neuromodulation technique with good spatial selectivity. We report an experimental investigation to detect noninvasive electrophysiological response induced by low-intensity tFUS in an in vivo animal model and perform electrophysiological source imaging (ESI) of tFUS-induced brain activity from noninvasive scalp EEG recordings. Methods: A single-element ultrasound transducer was used to generate low-intensity tFUS ( ) and induce brain activation at multiple selected sites in an in vivo rat model. Up to 16 scalp electrodes were used to record tFUS-induced EEG. Event-related potentials were analyzed in time, frequency, and spatial domains. Current source distributions were estimated by ESI to reconstruct spatiotemporal distributions of brain activation induced by tFUS. Results: Neuronal activation was observed following low-intensity tFUS, as correlated to tFUS intensity and sonication duration. ESI revealed initial focal activation in cortical area corresponding to tFUS stimulation site and the activation propagating to surrounding areas over time. Conclusion: The present results demonstrate the feasibility of noninvasively recording brain electrophysiological response in vivo following low-intensity tFUS stimulation, and the feasibility of imaging spatiotemporal distributions of brain activation as induced by tFUS in vivo . Significance: The present approach may lead to a new means of imaging brain activity using tFUS perturbation and a closed-loop ESI-guided tFUS neuromodulation modality.

Description: Objective: Superharmonic contrast-enhanced ultrasound imaging, also called acoustic angiography, has previously been used for the imaging of microvasculature. This approach excites microbubble contrast agents near their resonance frequency and receives echoes at nonoverlapping superharmonic bandwidths. No integrated system currently exists could fully support this application. To fulfill this need, an integrated dual-channel transmit/receive system for superharmonic imaging was designed, built, and characterized experimentally. Method: The system was uniquely designed for superharmonic imaging and high-resolution B-mode imaging. A complete ultrasound system including a pulse generator, a data acquisition unit, and a signal processing unit were integrated into a single package. The system was controlled by a field-programmable gate array, on which multiple user-defined modes were implemented. A 6-, 35-MHz dual-frequency dual-element intravascular ultrasound transducer was designed and used for imaging. Result: The system successfully obtained high-resolution B-mode images of coronary artery ex vivo with 45-dB dynamic range. The system was capable of acquiring in vitro superharmonic images of a vasa vasorum mimicking phantom with 30-dB contrast. It could detect a contrast agent filled tissue mimicking tube of 200 μm diameter. Conclusion: For the first time, high-resolution B-mode images and superharmonic images were obtained in an intravascular phantom, made possible by the dedicated integrated system proposed. The system greatly reduced the cost and complexity of the superharmonic imaging intended for preclinical study. Significant: The system showed promise for high-contrast intravascular microvascular imaging, which may have significant importance in assessment of the vasa vasorum associated with atherosclerotic plaques.

Description: Goal: Although photoplethysmographic (PPG) signals can monitor heart rate (HR) quite conveniently in hospital environments, trying to incorporate them during fitness programs poses a great challenge, since in these cases, the signals are heavily corrupted by motion artifacts. Methods: In this paper, we present a novel signal processing framework which utilizes two channel PPG signals and estimates HR in two stages. The first stage eliminates any chances of a runaway error by resorting to an absolute criterion condition based on ensemble empirical mode decomposition. This stage enables the algorithm to depend very little on the previously estimated HR values and to discard the need of an initial resting phase. The second stage, on the other hand, increases the algorithm's robustness against offtrack errors by using recursive least squares filters complemented with an additional novel technique, namely time-domain extraction. Results: Using this framework, an average absolute error of 1.02 beat per minute (BPM) and standard deviation of 1.79 BPM are recorded for 12 subjects performing a run with peak velocities reaching as high as 15 km/h. Conclusion: The performance of this algorithm is found to be better than the other recently reported algorithms in this field such as TROIKA and JOSS. Significance: This method is expected to greatly facilitate the presently available wearable gadgets in HR computation during various physical activities.

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods.

Description: Goal: Whole-exome sequencing provides a more cost-effective way than whole-genome sequencing for detecting genetic variants, such as copy number variations (CNVs). Although a number of approaches have been proposed to detect CNVs from whole-genome sequencing, a direct adoption of these approaches to whole-exome sequencing will often fail because exons are separately located along a genome. Therefore, an appropriate method is needed to target the specific features of exome sequencing data. Methods: In this paper, a novel sparse model based method is proposed to discover CNVs from multiple exome sequencing data. First, exome sequencing data are represented with a penalized matrix approximation, and technical variability and random sequencing errors are assumed to follow a generalized Gaussian distribution. Second, an iteratively reweighted least squares algorithm is used to estimate the solution. Results: The method is tested and validated on both synthetic and real data, and compared with other approaches including CoNIFER, XHMM, and cn.MOPS. The test demonstrates that the proposed method outperform other approaches. Conclusion: The proposed sparse model can detect CNVs from exome sequencing data with high power and precision. Significance: Sparse model can target the specific features of exome sequencing data. The software codes are freely available at http://www.tulane.edu/ wyp/software/Exon_CNV.m

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.