ALBERT

Description: Objective: To develop a pipeline for realistic head models of nonhuman primates (NHPs) for simulations of noninvasive brain stimulation, and use these models together with empirical threshold measurements to demonstrate that the models capture individual anatomical variability. Methods: Based on structural MRI data, we created models of the electric field (E-field) induced by right unilateral (RUL) electroconvulsive therapy (ECT) in four rhesus macaques. Individual motor threshold (MT) was measured with transcranial electric stimulation (TES) administered through the RUL electrodes in the same subjects. Results: The interindividual anatomical differences resulted in 57% variation in median E-field strength in the brain at fixed stimulus current amplitude. Individualization of the stimulus current by MT reduced the E-field variation in the target motor area by 27%. There was significant correlation between the measured MT and the ratio of simulated electrode current and E-field strength ( $r^{2} = 0.95$ , $p = 0.026$ ). Exploratory analysis revealed significant correlations of this ratio with anatomical parameters including of the superior electrode-to-cortex distance, vertex-to-cortex distance, and brain volume ( $r^{2} > 0.96$ , $p 〈 0.02$ ). The neural activation threshold was estimated to be $0.45 pm 0.07$ V/cm for 0.2-ms stimulus pulse width. Conclusion: These results suggest that our individual-specific NHP E-field models appropriately capture individual anatomical variability relevant to the dosing of TES/ECT. These findings are exploratory due to the small number of subjects. Sign- ficance: This study can contribute insight in NHP studies of ECT and other brain stimulation interventions, help link the results to clinical studies, and ultimately lead to more rational brain stimulation dosing paradigms.

Description: Ectopic electrical activity that originates in the peri-infarct region can give rise to potentially lethal re-entrant arrhythmias. The spatial variation in electrotonic loading that results from structural remodelling in the infarct border zone may increase the probability that focal activity will trigger electrical capture, but this has not previously been investigated systematically. This study uses in-silico experiments to examine the structural modulation of effective refractory period on ectopic beat capture. Informed by 3-D reconstructions of myocyte organization in the infarct border zone, a region of rapid tissue expansion is abstracted to an idealized representation. A novel metric is introduced that defines the local electrotonic loading as a function of passive tissue properties and boundary conditions. The effective refractory period correlates closely with local electrotonic loading, while the action potential duration, conduction, and upstroke velocity reduce in regions of increasing electrotonic load. In the presence of focal ectopic stimuli, spatial variation in effective refractory period can cause unidirectional conduction block providing a substrate for reentrant arrhythmias. Consequently, based on the observed results, a possible novel mechanism for arrhythmogenesis in the infarct border zone is proposed.

Description: Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. $F_1$ scores of $mathbf {98.3%}$ and $mathbf {99.5%}$ were found on each database, respectively, which are superior to other published algorithms’ results reported on the same databases. Only $mathbf {3%}$ of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.

Description: Objective : A hybrid imaging technique, ultrasound-modulated luminescence tomography, that uses ultrasound to modulate diffusely propagating light has been shown to improve the spatial resolution of optical images. This paper investigates the underlying modulation mechanisms and the feasibility of applying this technique to improve spatial resolution in bioluminescence tomography. Methods : Ultrasound-modulated bioluminescence tomography was studied numerically to identify the effects of four factors (reduced optical scattering coefficient, optical absorption coefficient, refractive index, and luciferase concentration) on the depth of light modulation. In practice, an open source finite-element method tool for simulation of diffusely propagating light, near infrared fluorescence and spectral tomography, was modified to incorporate the effects of ultrasound modulation. The signal-to-noise ratios of detected modulated bioluminescent emissions are calculated using the optical and physical properties of a mouse model. Results : The modulation depth of the bioluminescent emission affected by the US induced variation of local concentration of the light emitting enzyme luciferase was at least two orders of magnitude greater than that caused by variations in the other factors. For surface radiances above approximately $10^7$ $hbox{photons}$ / $hbox{s}$ / $hbox{cm}^{2}$ / $hbox{sr,}$ the corresponding SNRs are detectable with the currently available detector technologies. Conclusion : The dominant effect in generation of ultrasound-modulated bioluminescence is ultrasound induced variation in luciferase concentration. The SNR analysis confirms the- feasibility of applying ultrasound-modulated bioluminescence tomography in preclinical imaging of mice. Significance : The simulation model developed suggests ultrasound-modulated bioluminescence tomography is a potential technique to improve the spatial resolution of bioluminescence tomography.

Description: Mechanical ventilation of patients with acute respiratory distress syndrome (ARDS) is a necessary life support measure which may lead to ventilator-induced lung injury, a complication that can be reduced or ameliorated by using appropriate tidal volumes and positive end-expiratory pressures. However, the optimal mechanical ventilation parameters are almost certainly different for each patient, and will vary with time as the injury status of the lung changes. In order to optimize mechanical ventilation in an individual ARDS patient, therefore, it is necessary to track the manner in which injury status is reflected in the mechanical properties of the lungs. Accordingly, we developed an algorithm for assessing the time-dependent manner in which different lung regions open (recruit) and close (derecruit) as a function of the pressure waveform that is applied to the airways during mechanical ventilation. We used this algorithm to test the notion that variable ventilation provides the dynamic perturbations in lung volume necessary to accurately identify recruitment/derecruitment dynamics in the injured lung. We performed this test on synthetic pressure and flow data generated with established numerical models of lung function corresponding to both healthy mice and mice with lung injury. The data were generated by subjecting the models to a variety of mechanical ventilation regimens including variable ventilation. Our results support the hypothesis that variable ventilation can be used as a diagnostic tool to identify the injury status of the lung in ARDS.

Description: Goal: Many brain–computer interface (BCI) classification techniques rely on a large number of labeled brain responses to create efficient classifiers. A large database representing all of the possible variability in the signal is impossible to obtain in a short period of time, and prolonged calibration times prevent efficient BCI use. We propose to improve BCIs based on the detection of event-related potentials (ERPs) in two ways. Methods: First, we increase the size of the training database by considering additional deformed trials. The creation of the additional deformed trials is based on the addition of Gaussian noise, and on the variability of the ERP latencies. Second, we exploit the variability of the ERP latencies by combining decisions across multiple deformed trials. These new methods are evaluated on data from 16 healthy subjects participating in a rapid serial visual presentation task. Results: The results show a significant increase in the performance of single-trial detection with the addition of artificial trials, and the combination of decisions obtained from altered trials. When the number of trials to train a classifier is low, the proposed approach allows us improve performance from an AUC of $0.533pm 0.080$ to $0.905pm 0.053$ . This improvement represents approximately an 80% reduction in classification error. Conclusion: These results demonstrate that artificially increasing the training dataset leads to improved single-trial detection. Significance: Calibration sessions can be shortened for BCIs based on ERP detection.

Description: Goal: The existing ISFET-based DNA sequencing detects hydrogen ions released during the polymerization of DNA strands on microbeads, which are scattered into microwell array above the ISFET sensor with unknown distribution. However, false pH detection happens at empty microwells due to crosstalk from neighboring microbeads. In this paper, a dual-mode CMOS ISFET sensor is proposed to have accurate pH detection toward DNA sequencing. Methods: Dual-mode sensing, optical and chemical modes, is realized by integrating a CMOS image sensor (CIS) with ISFET pH sensor, and is fabricated in a standard 0.18-μm CIS process. With accurate determination of microbead physical locations with CIS pixel by contact imaging, the dual-mode sensor can correlate local pH for one DNA slice at one location-determined microbead, which can result in improved pH detection accuracy. Moreover, toward a high-throughput DNA sequencing, a correlated-double-sampling readout that supports large array for both modes is deployed to reduce pixel-to-pixel nonuniformity such as threshold voltage mismatch. Results: The proposed CMOS dual-mode sensor is experimentally examined to show a well correlated pH map and optical image for microbeads with a pH sensitivity of 26.2 mV/pH, a fixed pattern noise (FPN) reduction from 4% to 0.3%, and a readout speed of 1200 frames/s. Conclusion: A dual-mode CMOS ISFET sensor with suppressed FPN for accurate large-arrayed pH sensing is proposed and demonstrated with state-of-the-art measured results toward accurate and high-throughput DNA sequencing. Significance: The developed dual-mode CMOS ISFET sensor has great potential for future personal genome diagnostics with high accuracy and low cost.

Description: Goal : Visual feedback can be used during gait rehabilitation to improve the efficacy of training. We presented a paradigm called visual feedback distortion; the visual representation of step length was manipulated during treadmill walking. Our prior work demonstrated that an implicit distortion of visual feedback of step length entails an unintentional adaptive process in the subjects’ spatial gait pattern. Here, we investigated whether the implicit visual feedback distortion, versus conscious correction, promotes efficient locomotor adaptation that relates to greater retention of a task. Methods: Thirteen healthy subjects were studied under two conditions: (1) we implicitly distorted the visual representation of their gait symmetry over 14 min, and (2) with help of visual feedback, subjects were told to walk on the treadmill with the intent of attaining the gait asymmetry observed during the first implicit trial. After adaptation, the visual feedback was removed while subjects continued walking normally. Over this 6-min period, retention of preserved asymmetric pattern was assessed. Results: We found that there was a greater retention rate during the implicit distortion trial than that of the visually guided conscious modulation trial. Conclusion: This study highlights the important role of implicit learning in the context of gait rehabilitation by demonstrating that training with implicit visual feedback distortion may produce longer lasting effects. Significance: This suggests that using visual feedback distortion could improve the effectiveness of treadmill rehabilitation processes by influencing the retention of motor skills.

Description: This paper explores the development of biomechanical models for evaluating a new class of passive mechanical implants for orthopedic surgery. The proposed implants take the form of passive engineered mechanisms, and will be used to improve the functional attachment of muscles to tendons and bone by modifying the transmission of forces and movement inside the body. Specifically, we present how two types of implantable mechanisms may be modeled in the open-source biomechanical software OpenSim. The first implant, which is proposed for hand tendon-transfer surgery, differentially distributes the forces and movement from one muscle across multiple tendons. The second implant, which is proposed for knee-replacement surgery, scales up the forces applied to the knee joint by the quadriceps muscle. This paper's key innovation is that such mechanisms have never been considered before in biomechanical simulation modeling and in surgery. When compared with joint function enabled by the current surgical practice of using sutures to make the attachment, biomechanical simulations show that the surgery with 1) the differential mechanism (tendon network) implant improves the fingers’ ability to passively adapt to an object's shape significantly during grasping tasks (2.74× as measured by the extent of finger flexion) for the same muscle force, and 2) the force-scaling implant increases knee-joint torque by 84% for the same muscle force. The critical significance of this study is to provide a methodology for the design and inclusion of the implants into biomechanical models and validating the improvement in joint function they enable when compared with current surgical practice.

Description: The impact of pulse repetition rate (PRR) in modulating electroporation (EP) induced by nanosecond pulsed electric fields (nsPEFs) in mammalian cells was approached here by performing both biological and numerical analysis. Plasma membrane permeabilization and viability of Jurkat cells were analyzed after exposure to 500, 1.3 MV/m, 40 ns PEFs with variable PRR (2–30 Hz). A finite-element model was used to investigate EP dynamics in a single cell under the same pulsing conditions, by looking at the time course of transmembrane voltage and pore density on the ns time scale. The biological observations showed an increased EP and reduced viability of the exposed cells at lower PRR in the considered range. The numerical analysis resulted in different dynamics of plasma membrane response when ns pulses were delivered with different PRR, consistently with a phenomenon of electrodesensitization recently hypothesized by another research group.

Description: Gastroscopy plays an important role in the diagnosis of gastric disease. In this paper, we develop an image panoramic system to assist endoscopists in improving lesion surveillance and reducing many of the tedious operations associated with gastroscopy. The constructed panoramic view has two categories: 1) the local view broadens the endoscopist's field of view in real time. Combining with the original gastroscopic video, this mosaicking view enables the endoscopist to diagnose the lesion comprehensively; 2) the global view constructs a large-area panoramic scene of the internal gastric surface, which can be used for intraoperative surgical navigation and postoperative scene review. Due to the irregular texture and inconsistent reflection of the gastric internal surface, common registration methods cannot accurately stitch this surface. Thereby, a six degree of freedom position tracking endoscope is devised to accommodate for the accumulated mosaicking error and provide efficient mosaicking results. For the global view, a dual-cube constraint model and a Bundle Adjustment algorithm are incorporated to deal with the mosaicking error caused by the irregular inflation and nonrigid deformation of the stomach. Moreover, texture blending and frame selection schemes are developed to make the mosaicking results feasible in real-clinical applications. The experimental results demonstrate that our system performs with a speed of 7.12 frames/s in a standard computer environment, and the mosaicking mean error is 0.43 mm for local panoramic view and 3.71 mm for global panoramic view.

Description: In this paper, we present a smart capsule for location-specific drug release in the gastrointestinal tract. Once activated through a magnetic proximity fuse, the capsule opens up and releases its powdered payload in a location specified by an implanted miniature magnetic marker or an externally worn larger magnet. The capsule (9 mm × 26 mm) comprises of two compartments: one contains a charged capacitor and a reed switch, while the second one houses the drug reservoir capped by a taut nylon thread intertwined with a nichrome wire. The nichrome wire is connected to the capacitor through the reed switch. The capacitor is charged to 2.7 V before ingestion and once within the proximity of the permanent magnet; the reed switch closes, discharging the capacitor through the nichrome wire, melting the nylon thread, detaching the cap, and emptying the drug reservoir.

Description: Goal: The purpose of this paper was to evaluate a nitinol tine fixation design for a transcatheter pacemaker in order to determine if the tines could be easily deployed and safely removed from the myocardium, enable low, stable pacing thresholds, and minimize the potential for dislodgment. Methods: The penetration properties of 13 human hearts were compared to the deployment and fixation energy of the tines to determine if the tines could be easily deployed and removed from the myocardium. The safety factor for dislodgement was calculated by comparing the kinetic energy of the device to the fixation energy of the tines. The fixation stability was tested in 113 chronic implants across 89 animals via pacing threshold measurements or evidence of dislodgement at necropsy. Results: Based on the tine fixation and tissue energy analysis, the tines can easily penetrate the heart. The tines can be safely removed from the myocardium based on the increased tine surface area during retraction. There were no dislodgements observed in the animals and the mean pacing threshold at implant was 0.59 +/− 0.21 V and at termination was 0.65 +/− 0.36 V. The safety factor for dislodgement was determined to be 15X during simulated exercise conditions. Conclusion: The nitinol tine fixation design enabled the implant of a self-contained pacemaker within the right ventricle and was effective in meeting the design requirements. Significance: This fixation technology provides a novel solution to enable the attachment of a transcatheter pacemaker directly within the heart.

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

Description: Presents corrections to the article, ???Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: Initial results for vocal fold nodules,??? (Ghassemi, M., et al), IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 1668???1675, Jun. 2014.

Description: Fabricated vessel-mimetic microtubes are essential for delivering sufficient nutrient to engineered composite tissues. In this paper, vascular-like microtubes are engineered by automated assembly of donut-shaped micromodules that embed fibroblast cells. A microrobotic system is set up with dual manipulators of 30-nm positioning resolution under an optical microscope. The system assembles the micromodules by repeated single-step pick-up motions. This process is specifically designed to avoid human interference and ensure high reproducibility for automation. We optimized the single-step motion by calibrating the key parameters (the micromodule dimensions) in a force analysis. The optimal motion achieved a 98% pick-up success rate. The automated repetitive single-step assembly is achieved by an algorithm that acquires the 3-D location and tracks the micromanipulator without being affected by low contrast. The accuracy of the acquired 3-D location was experimentally determined as approximately 1 pixel (2 μm under 4× magnification), and the tracking under different observation conditions is proved effective. Finally, we automatically assembled microtubes at 6 micromodules/min, sufficiently fast for fabricating macroscopic vessel-mimetic substitutes in biological applications.

Description: Goal: This study tests and validates a new method to remove power line interference from monopolar EMGs detected by multichannel systems: the filtered virtual reference (FVR). FVR is an adaptation of the virtual reference (VR) method, which consists in referencing signals detected by each electrode in a grid to their spatial average. Signals may however be distorted with the VR approach, in particular when the skin region where the detection system is positioned does not cover the entire muscle. Methods: Simulated and experimental EMGs were used to compare the performance of FVR and VR in terms of interference reduction and distortion of monopolar signals referred to a remote reference. Results : Simulated data revealed the monopolar EMG signals processed with FVR were significantly less distorted than those filtered by VR. These results were similarly observed for experimental signals. Moreover, FVR method outperformed VR in removing power line interference when it was distributed unevenly across the signals of the grid. Conclusion: Key results demonstrated that FVR improves the VR method as it reduces interference while preserving the information content of monopolar signals. Significance: Although the actual distribution of motor unit action potential is represented in monopolar EMGs, collecting high quality monopolar signals is challenging. This study presents a possible solution to this issue; FVR provides undistorted monopolar signals with negligible interference and is insensitive to muscle architecture. It is therefore relevant for EMG applications benefiting from a clean monopolar detection (e.g., decomposition, control of prosthetic devices, motor unit number estimation).

Description: This study concentrates on finite-element-method (FEM)-based electroencephalography (EEG) forward simulation in which the electric potential evoked by neural activity in the brain is to be calculated at the surface of the head. The main advantage of the FEM is that it allows realistic modeling of tissue conductivity inhomogeneity. However, it is not straightforward to apply the classical model of a dipolar source with the FEM, due to its strong singularity and the resulting irregularity. The focus of this study is on comparing different methods to cope with this problem. In particular, we evaluate the accuracy of Whitney (Raviart–Thomas)-type dipole-like source currents compared to two reference dipole modeling methods: the St. Venant and partial integration approach. Common to all these methods is that they enable direct approximation of the potential field utilizing linear basis functions. In the present context, Whitney elements are particularly interesting, as they provide a simple means to model a divergence-conforming primary current vector field satisfying the square integrability condition. Our results show that a Whitney-type source model can provide simulation accuracy comparable to the present reference methods. It can lead to superior accuracy under optimized conditions with respect to both source location and orientation in a tetrahedral mesh. For random source orientations, the St. Venant approach turns out to be the method of choice over the interpolated version of the Whitney model. The overall moderate differences obtained suggest that practical aspects, such as the focality, should be prioritized when choosing a source model.

Description: This paper presents a wearable vital signs monitor at the ear. The monitor measures the electrocardiogram (ECG), ballistocardiogram (BCG), and photoplethysmogram (PPG) to obtain pre-ejection period (PEP), stroke volume (SV), cardiac output (CO), and pulse transit time (PTT). The ear is demonstrated as a natural anchoring point for the integrated sensing of physiological signals. All three signals measured can be used to obtain heart rate (HR). Combining the ECG and BCG allows for the estimation of the PEP, while combining the BCG and PPG allows for the measurement of PTT. Additionally, the J-wave amplitude of the BCG is correlated with the SV and, when combined with HR, yields CO. Results from a clinical human study on 13 subjects demonstrate this proof-of-concept device.

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: In recent years, in an attempt to maximize performance, machine learning approaches for event-related potential (ERP) spelling have become more and more complex. In this paper, we have taken a step back as we wanted to improve the performance without building an overly complex model, that cannot be used by the community. Our research resulted in a unified probabilistic model for ERP spelling, which is based on only three assumptions and incorporates language information. On top of that, the probabilistic nature of our classifier yields a natural dynamic stopping strategy. Furthermore, our method uses the same parameters across 25 subjects from three different datasets. We show that our classifier, when enhanced with language models and dynamic stopping, improves the spelling speed and accuracy drastically. Additionally, we would like to point out that as our model is entirely probabilistic, it can easily be used as the foundation for complex systems in future work. All our experiments are executed on publicly available datasets to allow for future comparison with similar techniques.

Description: Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in different domains, piecewise smoothness, low rank, etc. We propose a framework to exploit all the available structure information. A new convex programming problem is generated with multiple convex structure-inducing constraints and the linear measurement fitting constraint. With additional a priori information for solving the underdetermined system, the signal recovery performance can be improved. In numerical experiments, we compare the proposed method with classical methods. Both simulated data and real-life biomedical data are used. Results show that the newly proposed method achieves better reconstruction accuracy performance in term of both L1 and L2 errors.

Description: Transcranial magnetic stimulation (TMS) is a tool for noninvasive stimulation of neuronal tissue used for research in cognitive neuroscience and to treat neurological disorders. Many TMS applications call for large electric fields to be sharply focused on regions that often lie deep inside the brain. Unfortunately, the fields generated by present-day TMS coils diffuse and decay rapidly as they penetrate into the head. As a result, they tend to stimulate relatively large regions of tissue near the brain surface. Earlier studies suggested that a focused TMS excitation can be attained using multiple nonuniformly fed coils in a multichannel array. We propose a systematic, genetic algorithm-based technique for synthesizing multichannel arrays that minimize the volume of the excited region required to achieve a prescribed penetration depth and maintain realistic values for the input driving currents. Because multichannel arrays are costly to build, we also propose a method to convert the multichannel arrays into single-channel ones while minimally materially deteriorating performance. Numerical results show that the new multi- and single-channel arrays stimulate tissue 2.4 cm into the head while exciting 3.0 and 2.6 times less volume than conventional Figure-8 coils, respectively.

Description: In difficult epileptic patients, the brain structures are explored by means of depth multicontact electrodes [stereoelectroencephalography (SEEG)]. Recently, a novel diagnostic technique allows an accurate definition of the epileptogenic zone using deep brain stimulation (DBS). The stimulation signal propagates in the brain and thus it appears on most of the other SEEG electrodes, masking the local brain electrophysiological activity. The objective of this paper is the DBS-SEEG signals detrending and denoising in order to recover the masked physiological sources. We review the main filtering methods and put forward an approach based on the combination of filtering with generalized eigenvalue decomposition (GEVD). An experimental study on simulated and real SEEG shows that our approach is able to separate DBS sources from brain activity. The best results are obtained by an original singular spectrum analysis-GEVD approach.

Description: This paper presents a novel palpation probe based on optical fiber technology. It is designed to measure stiffness distribution of a soft tissue while sliding over the tissue surface in a near frictionless manner. A novelty of the probe is its ability to measure indentation depth for nonplanar tissue profiles which are commonly experienced during surgery. Since tumors are often harder than the surrounding tissue, the proposed probe can intraoperatively aid the surgeon to rapidly identify the presence, location, and size of the tumors through the generation of a tissue stiffness map. The probe can concurrently measure tissue reaction force, indentation depth, and the orientation of the probe with respect to the tissue surface. Hence, it can generate an elasticity model of the tissue with minimum measurement inaccuracies caused by surface profile variations. Further, the probe has a tunable force range and the indentation force can be adjusted externally to match tissue limitations. The performance of the probe developed was validated using simulated soft tissues samples. Our tumor identification experiments showed that the probe can accurately identify the location and size of tumors hidden inside nonflat tissue surfaces. Further, the probe has clearly demonstrated its potential to identify tumors with tumor–tissue stiffness ratios as low as 2.1.

Description: We introduce a novel method for the automatic detection of atrial fibrillation (AF) using time-varying coherence functions (TVCF). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). The two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF; the second TVTF is produced by reversing the input and output signals. We found that the resultant TVCF between two adjacent normal sinus rhythm (NSR) segments shows high coherence values (near 1) throughout the entire frequency range. However, if either or both segments partially or fully contain AF, the resultant TVCF is significantly lower than 1. When TVCF was combined with Shannon entropy (SE), we obtained even more accurate AF detection rate of 97.9% for the MIT-BIH atrial fibrillation (AF) database $(n$ = 23) with 128 beat segments. The detection algorithm was tested on four databases using 128 beat segments: the MIT-BIH AF database, the MIT-BIH NSR database ( $n$ = 18), the MIT-BIH Arrhythmia database ( $n$ = 48), and a clinical 24-h Holter AF database ( $n$ = 25). Using the receiver operating characteristic curves from the combination of TVCF and SE, we obtained a sensitivity of 98.2% and specificity of 97.7% for the MIT-BIH AF database. For the MIT-BIH NSR database, we found a specificity of 99.7%. For the MIT-BIH Arrhythmia database, the sensitivity and specificity were 91.1% and 89.7%, respectively. For the clinical database (24-h Holter data), the sensitivity and specificity were 92.3% and 93.6%, respectively. We - lso found that a short segment (12 beats) also provided accurate AF detection for all databases: sensitivity of 94.7% and specificity of 90.4% for the MIT-BIH AF, specificity of 94.4% for the MIT-BIH-NSR, the sensitivity of 92.4% and specificity of 84.1% for the MIT-BIH arrhythmia, and sensitivity of 93.9% and specificity of 84.4% for the clinical database. The advantage of using a short segment is more accurate AF burden calculation as the timing of transitions between NSR and AF are more accurately detected.

Description: In functional MRI, it is often desirable to reduce the readout duration to make the acquired data less prone to $T_2^*$ susceptibility artifacts. In addition, a shorter readout length allows for a shorter minimum TE, which is important for optimizing SNR. This can be achieved by undersampling the $k$ -space. However, the conventional Fourier transform-based reconstruction method suffers from under-sampling artifacts such as high-frequency ringing and loss of resolution. To address this problem, we revisit the constrained-model approach using the generalized-series (GS) which has been proposed to address the undersampling problem for dynamic MRI. We propose a modification to the conventional use of the model in order to reflect small hemodynamic signal changes typical in fMRI. Specifically, while realizing that having high model order is necessary to capture missing information, we found that it is not necessary to span all frequencies of GS basis functions uniformly. Instead, having $k$ -space and GS “sampling” trajectories covering low-frequencies uniformly while spanning high-frequencies sparsely, was observed to be an efficient strategy. The ability of the method over the conventional GS approach in improving resolution of functional images and activation maps while reducing undersampling ringing is demonstrated by simulations and experiments at $3$ T. Reduction in the readout time allowed an increase of statistical signal power as compared to the fully sampled acquisition. Unlike compressed sensing approaches, the proposed method is linear and hence has lower computational complexity. The method could prove useful for other imaging modalities where the signal change is smaller than the ba- eline component.

Description: Interbreath interval (IBI), the time interval between breaths, is an important measure used to analyze irregular breathing patterns in neonates. The discrete bursts of neural activity generate the IBI time series, which exhibits stochastic as well as deterministic dynamics. To quantify the irregularity of breathing, we propose a point process model of IBI using a comprehensive stochastic dynamic modeling framework. The IBIs of immature breathing patterns exhibit a long tail distribution and within a point process model, we have considered the lognormal distribution to represent the stochastic IBI characteristics. An autoregressive (AR) function is embedded within the model to capture the short-term IBI dynamics including abrupt IBI prolongations related to sporadic and periodic apneas that are common in neonates. We tested the utility of our paradigm for depicting the respiratory dynamics in neonatal rats and in preterm infants. Kolmogorov–Smirnov (KS) and independence tests reveal that the model accurately tracks the dynamic characteristics of the signals. In preterm infants, our model-derived indices of IBI instability strongly correlate with clinically derived indices of maturation. Our results validate a new class of algorithms, based on the point process theory, for defining instantaneous measures of breathing irregularity in neonates.

Description: Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement ( $pm hbox{1.96}sigma$ ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98% correct, and for vigorous motion (stepping) from less than 11% to more than 48% correct. We expect the greatly improved robustness to considerably widen the application scope of the technology.

Description: A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed.

Description: Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: $0.94 pm 0.08$ ) and “Euler characteristic” (AUC: $0.94 pm 0.07$ ), and GLCM-derived feature “Correlation” (AUC: $0.93 pm 0.07$ ). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

Description: We have developed a pupil-corneal reflection method-based gaze detection system, which allows large head movements and achieves easy gaze calibration. This system contains two optical systems consisting of components such as a camera and a near-infrared light source attached to the camera. The light source has two concentric LED rings with different wavelengths. The inner and outer rings generate bright and dark pupil images, respectively. The pupils are detected from a difference image created by subtracting the bright and dark pupil images. The light source also generates the corneal reflection. The 3-D coordinates of the pupils are determined by the stereo matching method using two optical systems. The vector from the corneal reflection center to the pupil center in the camera image is determined as ${bm r}$ . The angle between the line of sight and the line passing through the pupil center and the camera (light source) is denoted as $theta$ . The relationship $theta = k leftvert {bm r} rightvert$ is assumed, where $k$ is a constant. The theory implies that head movement of the user is allowed and facilitates the gaze calibration procedure. In the automatic calibration method, $k$ is automatically determined while the user looks around on the PC screen without fixating on any specific calibration target. In the one-point calibration method, the user is asked to fixate on one calibration target at the PC screen in order to correct the difference between the optical and visual axes. In the two-point calibration method, in order to correct the nonlinear relationship between $theta$ and $leftvert {bm r} rightvert$ , the user is asked to fixate on two targets. The experimental results show that the three proposed calibration methods improve the precision of gaze detection step by step. In addition, the average gaze error in the visual angle is less than 1 $^circ$ for the seven head positions of the user.

Description: We present an encapsulation scheme that combines atomic layer deposited (ALD) Al $_{2}$ O $_{3}$ and Parylene C for the encapsulation of implantable devices. The encapsulation performances of combining alumina and Parylene C was compared to individual layers of Parylene C or alumina and the bilayer coating had superior encapsulation properties. The alumina–Parylene coated interdigitated electrodes (IDEs) soaked in PBS for up to nine months at temperatures from 37 to 80 °C for accelerated lifetime testing. For 52-nm alumina and 6-μm Parylene C, leakage current was ∼20 pA at 5 VDC, and the impedance was about 3.5 MΩ at 1 kHz with a phase near −87° from electrochemical impedance spectroscopy for samples soaked at 67 °C for equivalent lifetime of 72 months at 37 °C. The change of impedance during the whole soaking period (up to 70 months of equivalent soaking time at 37 °C) over 1 to 10 $^{6}$  Hz was within 5%. The stability of impedance indicated almost no degradation of the encapsulation. Bias voltage effect was studied by continuously applying 5 VDC, and it reduced the lifetime of Parylene coating by ∼75% while it showed no measurable effect on the bilayer coating. Lifetime of encapsulation of IDEs with topography generated by attaching a coil and surface mount device (SMD) capacitor was about half of that of planer IDEs. The stable long-term insulation impedance, low leakage current, and better lifetime under bias voltage and topography made this double-layer encapsulation very promising for chronic implantable devices.

Description: Muscle sympathetic nerve activity is a primary source of cardiovascular control in humans. Traditional analyses smooth away the fine temporal structure of the sympathetic recordings, limiting our understanding of sympathetic activation mechanisms. We use multifiber spike trains extracted from standard microneurography voltage trace to characterize the sympathetic spiking at rest and during sympathoexcitation. Our analysis corroborates known features of sympathetic activity, such as bursting behavior, cardiac rhythmicity, and long conduction delays. It also elucidates new features such as large heartbeat-to-heartbeat variability of firing rates and precise pattern of spiking within cardiac cycles. We find that at low firing rates, spikes occur uniformly throughout the cardiac cycle, but at higher rates, they tend to cluster in bursts around a particular latency. This latency shortens and the clusters tighten as the firing rates grow. Sympathoexcitation increases firing rates and shifts the burst latency later. Negative rate/latency correlation and the sympathoexcitatory shift suggest that spike production of the individual fibers contributes significantly to the control of the sympathetic bursts strength. Access to fine scale temporal information, more physiologically accurate description of nerve activity, and new hypotheses about the nervous outflow control establishes sympathetic spiking as a valuable tool for the cardiovascular research.

Description: In this paper, we report the development of a flexible base array of penetrating electrodes which can be used to interface with the spinal cord. A customizable and feasible fabrication protocol is described. The flexible base arrays were fabricated and implanted into surrogate cords which were elongated by 12%. The resulting strains were optically measured across the cord and compared to those associated with two types of electrodes arrays (one without a base and one with a rigid base connecting the electrodes). The deformation behavior of cords implanted with the flexible base arrays resembled the behavior of cords implanted with individual microwires that were not connected through a base. The results of the strain test were used to validate a 2-D finite element model. The validated model was used to assess the stresses induced by the electrodes of the three types of arrays on the cord, and to examine how various design parameters (thickness, base modulus, etc.,) impact the mechanical behavior of the electrode array. Rigid base arrays induced higher stresses on the cord than the flexible base arrays which in turn imposed higher stresses than the individual microwire implants. The developed flexible base array showed improvement over the rigid base array; however, its stiffness needs to be further reduced to emulate the mechanical behavior of individual microwire arrays without a base.

Description: The dc component of biological sources is difficult to measure with body surface electrodes. This is due to the existence of an electrical polarization in the interface between the electrode and the electrolyte. A system to acquire a dc component of the biological origin is proposed by means of differentiating the polarization potential from the biological one. The acquisition system is comprised of an instrumentation amplifier, an integrated semiconductor analog switch, and ordinary silver/silver-chloride body surface electrodes. Biopotentials, electrooculograms from the eyes, were noninvasively recorded by the system with the analog switch which was inserted between the electrodes to make the two inputs electrically open following them being shorted. Asymptotes of a triple exponential function were subsequently extracted by regression model analysis on the output signals that were acquired immediately after the switch opened. One of the distinctive asymptotes of the function showed that the magnitude which corresponded to eye movements stayed stable as long as the eyes were fixed. Analysis of the time constants involved in the acquisition system elucidated that the asymptote was the biological origin.

Description: Tissue microarrays (TMAs) facilitate the survey of very large numbers of tumors. However, the manual assessment of stained TMA sections constitutes a bottleneck in the pathologist’s work flow. This paper presents a computational pipeline for automatically classifying and scoring breast cancer TMA spots that have been subjected to nuclear immunostaining. Spots are classified based on a bag of visual words approach. Immunohistochemical scoring is performed by computing spot features reflecting the proportion of epithelial nuclei that are stained and the strength of that staining. These are then mapped onto an ordinal scale used by pathologists. Multilayer perceptron classifiers are compared with latent topic models and support vector machines for spot classification, and with Gaussian process ordinal regression and linear models for scoring. Intraobserver variation is also reported. The use of posterior entropy to identify uncertain cases is demonstrated. Evaluation is performed using TMA images stained for progesterone receptor.

Description: The removal of spatially correlated noise is an important step in processing multichannel recordings. Here, a technique termed the adaptive common average reference (ACAR) is presented as an effective and simple method for removing this noise. The ACAR is based on a combination of the well-known common average reference (CAR) and an adaptive noise canceling (ANC) filter. In a convergent process, the CAR provides a reference to an ANC filter, which in turn provides feedback to enhance the CAR. This method was effective on both simulated and real data, outperforming the standard CAR when the amplitude or polarity of the noise changes across channels. In many cases, the ACAR even outperformed independent component analysis. On 16 channels of simulated data, the ACAR was able to attenuate up to approximately 290 dB of noise and could improve signal quality if the original SNR was as high as 5 dB. With an original SNR of 0 dB, the ACAR improved signal quality with only two data channels and performance improved as the number of channels increased. It also performed well under many different conditions for the structure of the noise and signals. Analysis of contaminated electrocorticographic recordings further showed the effectiveness of the ACAR.

Description: This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

Description: A brain–computer interface (BCI) based on steady-state visual-evoked potentials (SSVEP) has two difficulties: limitation of the number of commands and uneven probabilities of command execution. To address these problems, the present paper proposes a paradigm of BCI using frequency-modulated visual stimuli. The commands are translated into code words consisting of binary digits, to which visual stimuli with distinct frequencies are assigned. Frequencies of SSVEP are recognized to detect bits, and a command to be executed is determined from the sequence of detected bits. Experimental results show that the proposed paradigm achieves a reliable BCI with higher accuracies and balanced command executing probabilities.

Description: A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over steady-state and transient paradigms, and across a population of subjects. The respective steady-state responses are evaluated in terms of signal-to-noise ratio and statistical significance, while the qualitative analysis of the transient responses is performed by considering grand averaged event-related potential (ERP) waveforms. The outcomes of this study demonstrate conclusively that the ear-EEG signals, in terms of the signal-to-noise ratio, are on par with conventional EEG recorded from electrodes placed over the temporal region.

Description: Increasing the freedom of communication using conventional row/column (RC) P300 paradigm by naive way (increasing matrix size) may deteriorate inherent distraction effect and interaction speed. In this paper, we propose a two-level predictive (TLP) paradigm by integrating a $3times 3$ two-level matrix paradigm with a statistical language model. The TLP paradigm is evaluated using offline and online data from ten healthy subjects. Significantly larger event-related potentials (ERPs) are evoked by the TLP paradigm compared with the classical $6times 6$ RC. During an online task (correctly spell an English sentence with 57 characters), accuracy and information transfer rate for the TLP are increased by 14.45% and 29.29%, respectively, when compared with the $6times 6$ RC. Time to complete the task is also decreased by 24.61% using TLP. In sharp contrast, an $8times 8$ RC (naive extension of the $6times 6$ RC) consumed $19.18%$ more time than the classical $6times 6$ RC. Furthermore, the statistical language model is also exploited to improve classification accuracy in a Bayesian approach. The proposed Bayesian fusion method is tested offline on data from the online spelling tasks. The results show its potential improvement on single-trial ERP classification.

Description: This paper presents a class of hydration monitor that uses ultrathin, stretchable sheets with arrays of embedded impedance sensors for precise measurement and spatially multiplexed mapping. The devices contain miniaturized capacitive electrodes arranged in a matrix format, capable of integration with skin in a conformal, intimate manner due to the overall skin-like physical properties. These “epidermal” systems noninvasively quantify regional variations in skin hydration, at uniform or variable skin depths. Experimental results demonstrate that the devices possess excellent uniformity, with favorable precision and accuracy. Theoretical models capture the underlying physics of the measurement and enable quantitative interpretation of the experimental results. These devices are appealing for applications ranging from skin care and dermatology, to cosmetology and health/wellness monitoring, with the additional potential for combined use with other classes of sensors for comprehensive, quantitative physiological assessment via the skin.

Description: This paper describes a new subspace-based algorithm for the identification of Hammerstein systems. It extends a previous approach which described the Hammerstein cascade by a state-space model and identified it with subspace methods that are fast and require little a priori knowledge. The resulting state-space models predict the system response well but have many redundant parameters and provide limited insight into the system since they depend on both the nonlinear and linear elements. This paper addresses these issues by reformulating the problem so that there are many fewer parameters and each parameter is related directly to either the linear dynamics or the static nonlinearity. Consequently, it is straightforward to construct the continuous-time Hammerstein models corresponding to the estimated state-space model. Simulation studies demonstrated that the new method performs better than other well-known methods in the nonideal conditions that prevail during practical experiments. Moreover, it accurately distinguished changes in the linear component from those in the static nonlinearity. The practical application of the new algorithm was demonstrated by applying it to experimental data from a study of the stretch reflex at the human ankle. Hammerstein models were estimated between the velocity of ankle perturbations and the EMG activity of triceps surae for voluntary contractions in the plantarflexing and dorsiflexion directions. The resulting models described the behavior well, displayed the expected unidirectional rate sensitivity, and revealed that both the gain of the linear element and the threshold of the nonlinear changed with contraction direction.

Description: Lung needle biopsy image classification is a critical task for computer-aided lung cancer diagnosis. In this study, a novel method, multimodal sparse representation-based classification (mSRC), is proposed for classifying lung needle biopsy images. In the data acquisition procedure of our method, the cell nuclei are automatically segmented from the images captured by needle biopsy specimens. Then, features of three modalities (shape, color, and texture) are extracted from the segmented cell nuclei. After this procedure, mSRC goes through a training phase and a testing phase. In the training phase, three discriminative subdictionaries corresponding to the shape, color, and texture information are jointly learned by a genetic algorithm guided multimodal dictionary learning approach. The dictionary learning aims to select the topmost discriminative samples and encourage large disagreement among different subdictionaries. In the testing phase, when a new image comes, a hierarchical fusion strategy is applied, which first predicts the labels of the cell nuclei by fusing three modalities, then predicts the label of the image by majority voting. Our method is evaluated on a real image set of 4372 cell nuclei regions segmented from 271 images. These cell nuclei regions can be divided into five classes: four cancerous classes (corresponding to four types of lung cancer) plus one normal class (no cancer). The results demonstrate that the multimodal information is important for lung needle biopsy image classification. Moreover, compared to several state-of-the-art methods (LapRLS, MCMI-AB, mcSVM, ESRC, KSRC), the proposed mSRC can achieve significant improvement (mean accuracy of $hbox{88.1}%$ , precision of $hbox{85.2}%$ , recall of $hbox{92.8}%$ , etc.), especially for classi- ying different cancerous types.

Description: We present a needle deflection estimation method to anticipate needle bending during insertion into deformable tissue. Using limited additional sensory information, our approach reduces the estimation error caused by uncertainties inherent in the conventional needle deflection estimation methods. We use Kalman filters to combine a kinematic needle deflection model with the position measurements of the base and the tip of the needle taken by electromagnetic (EM) trackers. One EM tracker is installed on the needle base and estimates the needle tip position indirectly using the kinematic needle deflection model. Another EM tracker is installed on the needle tip and estimates the needle tip position through direct, but noisy measurements. Kalman filters are then employed to fuse these two estimates in real time and provide a reliable estimate of the needle tip position, with reduced variance in the estimation error. We implemented this method to compensate for needle deflection during simulated needle insertions and performed sensitivity analysis for various conditions. At an insertion depth of 150 mm, we observed needle tip estimation error reductions in the range of 28% (from 1.8 to 1.3 mm) to 74% (from 4.8 to 1.2 mm), which demonstrates the effectiveness of our method, offering a clinically practical solution.

Description: Myoelectric control algorithms have the potential to detect an amputee's motion intent and allow the prosthetic to adapt to changes in walking mode. The development of a myoelectric walking mode classifier for transtibial amputees is outlined. Myoelectric signals from four muscles (tibialis anterior, medial gastrocnemius (MG), vastus lateralis, and biceps femoris) were recorded for five nonamputee subjects and five transtibial amputees over a variety of walking modes: level ground at three speeds, ramp ascent/descent, and stair ascent/descent. These signals were decomposed into relevant features (mean absolute value, variance, wavelength, number of slope sign changes, number of zero crossings) over three subwindows from the gait cycle and used to test the ability of classification algorithms for transtibial amputees using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Detection of all seven walking modes had an accuracy of 97.9% for the amputee group and 94.7% for the nonamputee group. Misclassifications occurred most frequently between different walking speeds due to the similar nature of the gait pattern. Stair ascent/descent had the best classification accuracy with 99.8% for the amputee group and 100.0% for the nonamputee group. Stability of the developed classifier was explored using an electrode shift disturbance for each muscle. Shifting the electrode placement of the MG had the most pronounced effect on the classification accuracy for both samples. No increase in classification accuracy was observed when using SVM compared to LDA for the current dataset.

Description: A real-time dual-mode ultrasound array (DMUA) system for imaging and therapy is described. The system utilizes a concave (40-mm radius of curvature) 3.5 MHz, 32 element array, and modular multichannel transmitter/receiver. The system is capable of operating in a variety of imaging and therapy modes (on transmit) and continuous receive on all array elements even during high-power operation. A signal chain consisting of field-programmable gate arrays and graphical processing units is used to enable real time, software-defined beamforming and image formation. Imaging data, from quality assurance phantoms as well as in vivo small- and large-animal models, are presented and discussed. Corresponding images obtained using a temporally-synchronized and spatially-aligned diagnostic probe confirm the DMUA’s ability to form anatomically-correct images with sufficient contrast in an extended field of view around its geometric center. In addition, high-frame rate DMUA data also demonstrate the feasibility of detection and localization of echo changes indicative of cavitation and/or tissue boiling during high-intensity focused ultrasound exposures with 45–50 dB dynamic range. The results also show that the axial and lateral resolution of the DMUA are consistent with its $f_{rm number}$ and bandwidth with well-behaved speckle cell characteristics. These results point the way to a theranostic DMUA system capable of quantitative imaging of tissue property changes with high specificity to lesion formation using focused ultrasound.

Description: In medical ultrasound imaging, lateral resolution is limited when using a fixed transmit focusing. Various synthetic aperture (SA) techniques, in which two-way dynamic focusing is enabled by utilizing prebeamformed radio-frequency (RF) data have been proposed for improving the spatial resolution. However, SA methods were not extensively evaluated in terms of their clinical performance. In this paper, a phantom and an in vivo evaluation of the SA method with bidirectional pixel-based focusing (BiPBF) is presented in comparison with the conventional beamforming. The performance of the proposed SA-BiPBF was assessed with a blind study and the established breast imaging–reporting and data system (BI-RADS), in addition to measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Prebeamformed RF data were acquired from a tissue mimicking phantom (Model 040, CIRS Inc., Norfolk, VA, USA) and from patients with breast lesions by using a commercial ultrasound scanning system with a linear array transducer equipped with a research package and parallel data acquisition system (SonixTouch, SonixDAQ, and L14-5/38, Ultrasonix Corp., Canada). In phantom and in vivo experiments, a default setting of a breast preset was applied (e.g., the center frequency of 10 MHz and acoustic output of MI = 0.66). In phantom experiment, the SA-BiPBF method showed higher CNR and SNR values compared to the conventional method (3.4 and 23.9 dB versus 3.1 and 15.8 dB, respectively). In addition, the lateral resolution and penetration depth were increased by 95.4% and 40.3%, respectively. Consistent with the phantom experiment, in the in vivo experiment with ten patients, the CNR value for the SA method was 3.3 $ pm$ 0.5 compared to 2.8 $ pm$ 0.8 for the conventional method. Similarly, the SNR values- with the SA-BiPBF and conventional methods were 34.0 $ pm$ 3.6 and 27.2 $ pm$ 3.4 dB, respectively. From the experiments, it was shown in side-by-side comparisons that the image quality of the SA-BiPBF method was considerably improved in both phantom and in vivo breast images. However, the SA-BiPBF image showed different features compared to the conventional one in the in vivo experiments. These features are resulting from the increased image quality of the SA-BiPBF method but are not always perceived as improvements by the radiologists.

Description: Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this paper, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images.

Description: Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment; however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters $M$ -score and $R$ -value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation–maximization (EM) algorithm is developed for estimating CPs, $M$ -scores, and $R$ -values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.

Description: In computed tomography of liver tumors there is often heterogeneous density, weak boundaries, and the liver tumors are surrounded by other abdominal structures with similar densities. These pose limitations to accurate the hepatic tumor segmentation. We propose a level set model incorporating likelihood energy with the edge energy. The minimization of the likelihood energy approximates the density distribution of the target and the multimodal density distribution of the background that can have multiple regions. In the edge energy formulation, our edge detector preserves the ramp associated with the edges for weak boundaries. We compared our approach to the Chan–Vese and the geodesic level set models and the manual segmentation performed by clinical experts. The Chan–Vese model was not successful in segmenting hepatic tumors and our model outperformed the geodesic level set model. Our results on 18 clinical datasets showed that our algorithm had a Jaccard distance error of 14.4 ± 5.3%, relative volume difference of –8.1 ± 2.1%, average surface distance of 2.4 ± 0.8 mm, RMS surface distance of 2.9 ± 0.7 mm, and the maximum surface distance of 7.2 ± 3.1 mm.

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

Description: In some biomedical applications such as wireless capsule endoscopy, the localization of an in-body radio-frequency (RF) source is important for the positioning of any abnormality inside the gastrointestinal tract. With knowledge of the location, therapeutic operations can be performed precisely at the position of the abnormality. Electrical properties (relative permittivity and conductivity) of the tissues and their distribution are utilized to estimate the position. This paper presents a method for the localization of an in-body RF source based on microwave imaging. The electrical properties of the tissues and their distribution at 403.5 MHz are found from microwave imaging and the position of an RF source is then estimated based on the image. The method is applied on synthetic noisy data, obtained after the addition of white Gaussian noise to simulated data of a simple circular phantom, and a realistic phantom in a 2-D case. The root-mean-square of the error distance between the actual and the estimated position is found to be within 10 and 4 mm for the circular and the realistic phantom, respectively, showing the capability of the proposed algorithm to work with a good accuracy even in the presence of noise for the localization of the in-body RF source.

Description: Recent advances in high-density surface electromyogram (EMG) decomposition have made it a feasible task to discriminate single motor unit activity from surface EMG interference patterns, thus providing a noninvasive approach for examination of motor unit control properties. In the current study, we applied high-density surface EMG recording and decomposition techniques to assess motor unit firing behavior alterations poststroke. Surface EMG signals were collected using a 64-channel 2-D electrode array from the paretic and contralateral first dorsal interosseous (FDI) muscles of nine hemiparetic stroke subjects at different isometric discrete contraction levels between 2 to 10 N with a 2 N increment step. Motor unit firing rates were extracted through decomposition of the high-density surface EMG signals and compared between paretic and contralateral muscles. Across the nine tested subjects, paretic FDI muscles showed decreased motor unit firing rates compared with contralateral muscles at different contraction levels. Regression analysis indicated a linear relation between the mean motor unit firing rate and the muscle contraction level for both paretic and contralateral muscles (p 〈 0.001), with the former demonstrating a lower increment rate (0.32 pulses per second (pps)/N) compared with the latter (0.67 pps/N). The coefficient of variation (averaged over the contraction levels) of the motor unit firing rates for the paretic muscles (0.21 ± 0.012) was significantly higher than for the contralateral muscles (0.17 ± 0.014) (p 〈 0.05). This study provides direct evidence of motor unit firing behavior alterations poststroke using surface EMG, which can be an important factor contributing to hemiparetic muscle weakness.

Description: A new adaptive beamforming algorithm for imaging via small-aperture 1-D ultrasonic-phased arrays through composite layered structures is reported. Such structures cause acoustic phase aberration and wave refraction at undulating interfaces and can lead to significant distortion of an ultrasonic field pattern produced by conventional beamforming techniques. This distortion takes the form of defocusing the ultrasonic field transmitted through the barrier and causes loss of resolution and overall degradation of image quality. To compensate for the phase aberration and the refractional effects, we developed and examined an adaptive beamforming algorithm for small-aperture linear-phased arrays. After accurately assessing the barrier's local geometry and sound speed, the method calculates a new timing scheme to refocus the distorted beam at its original location. As a tentative application, implementation of this method for trans-skull imaging of certain types of head injuries through human skull is discussed. Simulation and laboratory results of applying the method on skull-mimicking phantoms are presented. Correction of up to 2.5 cm focal point displacement at up to 10 cm depth under our skull phantom is demonstrated. Quantitative assessment of the method in a variety of temporal focusing scenarios is also reported. Overall temporal deviation on the order of a few nanoseconds was observed between the simulated and experimental results. The single-point adaptive focusing results demonstrate strong potential of our approach for diagnostic imaging through intact human skull. The algorithms were implemented on an ultrasound advanced open-platform controlling 64 active elements on a 128-element phased array.