ALBERT

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: Provides a listing of current staff, committee members and society officers.

Beschreibung: We develop an approach to quantitative analysis of carbon dioxide concentration in exhaled breath, recorded as a function of time by capnography. The generated waveform—or capnogram—is currently used in clinical practice to establish the presence of respiration as well as determine respiratory rate and end-tidal CO 2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection. Prior approaches to quantitatively characterizing the capnogram shape have explored the correlation of various geometric parameters with pulmonary function tests. These studies attempted to characterize the capnogram in normal subjects and patients with cardiopulmonary disease, but no consistent progress was made, and no translation into clinical practice was achieved. We apply automated quantitative analysis to discriminate between chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), and between COPD and normal. Capnograms were collected from 30 normal subjects, 56 COPD patients, and 53 CHF patients. We computationally extract four physiologically based capnogram features. Classification on a hold-out test set was performed by an ensemble of classifiers employing quadratic discriminant analysis, designed through cross validation on a labeled training set. Using 80 exhalations of each capnogram record in the test set, performance analysis with bootstrapping yields areas under the receiver operating characteristic (ROC) curve of 0.89 (95% CI: 0.72–0.96) for COPD/CHF classification, and 0.98 (95% CI: 0.82–1.0) for COPD/normal classification. This classification performance is obtained with a run time sufficiently fast for real-time monitoring.

Beschreibung: In this study, we used muscle and motor unit indices, derived from convenient surface electromyography (EMG) measurements, for examination of paretic muscle changes post stroke. For 12 stroke subjects, compound muscle action potential and voluntary surface EMG signals were recorded from paretic and contralateral first dorsal interosseous, abductor pollicis brevis, and abductor digiti minimi muscles. Muscle activation index (AI), motor unit number index (MUNIX), and motor unit size index (MUSIX) were then calculated for each muscle. There was a significant AI reduction for all the three muscles in paretic side compared with contralateral side, providing an evidence of muscle activation deficiency after stroke. The hand MUNIX (defined by summing the values from the three muscles) was significantly reduced in paretic side compared with contralateral side, whereas the hand MUSIX was not significantly different. Furthermore, diverse changes in MUNIX and MUSIX were observed from the three muscles. A major feature of the present examinations is the primary reliance on surface EMG, which offers practical benefits because it is noninvasive, induces minimal discomfort and can be performed quickly.

Beschreibung: Robotic needle steering systems have the potential to greatly improve medical interventions, but they require new methods for medical image guidance. Three-dimensional (3-D) ultrasound is a widely available, low-cost imaging modality that may be used to provide real-time feedback to needle steering robots. Unfortunately, the poor visibility of steerable needles in standard grayscale ultrasound makes automatic segmentation of the needles impractical. A new imaging approach is proposed, in which high-frequency vibration of a steerable needle makes it visible in ultrasound Doppler images. Experiments demonstrate that segmentation from this Doppler data is accurate to within 1–2 mm. An image-guided control algorithm that incorporates the segmentation data as feedback is also described. In experimental tests in ex vivo bovine liver tissue, a robotic needle steering system implementing this control scheme was able to consistently steer a needle tip to a simulated target with an average error of 1.57 mm. Implementation of 3-D ultrasound-guided needle steering in biological tissue represents a significant step toward the clinical application of robotic needle steering.

Beschreibung: This paper presents a noncontact capacitive sensing system (C-Sens) for locomotion mode recognition of transtibial amputees. C-Sens detects changes in physical distance between the residual limb and the prosthesis. The sensing front ends are built into the prosthetic socket without contacting the skin. This novel signal source improves the usability of locomotion mode recognition systems based on electromyography (EMG) signals and systems based on capacitance signals obtained from skin contact. To evaluate the performance of C-Sens, we carried out experiments among six transtibial amputees with varying levels of amputation when they engaged in six common locomotive activities. The capacitance signals were consistent and stereotypical for different locomotion modes. Importantly, we were able to obtain sufficiently informative signals even for amputees with severe muscle atrophy (i.e., amputees lacking of quality EMG from shank muscles for mode classification). With phase-dependent quadratic classifier and selected feature set, the proposed system was capable of making continuous judgments about locomotion modes with an average accuracy of $96.3%$ and $94.8%$ for swing phase and stance phase, respectively (Experiment 1). Furthermore, the system was able to achieve satisfactory recognition performance after the subjects redonned the socket (Experiment 2). We also validated that C-Sens was robust to load bearing changes when amputees carried 5-kg weights during activities (Experiment 3). These results suggest that noncontact capacitive sensing is capable of circumventing practical problems of EMG systems without sacrificing performance and it is, thus, promising for automatic recognition of human motion intent for controlling powered prostheses.

Beschreibung: This study aims to analyze the protein aggregates spatial distribution for different cataract degrees, and correlate this information with the lens acoustical parameters and by this way, assess the cataract regional hardness. Different cataract degrees were induced ex vivo in porcine lenses. A 25 MHz ultrasonic transducer was used to obtain the acoustical parameters (velocity, attenuation, and backscattering signals). B-scan and Nakagami images were constructed. Also, lenses with different cataract degrees were sliced in two regions (nucleus and cortex), for fibers and collagen detection. A significant increase with cataract formation was found for the velocity, attenuation, and brightness intensity of the B-scan images and Nakagami m parameter ( $p 〈 0.01$ ). The acoustical parameters showed a good to moderate correlation with the m parameter for the different stages of cataract formation. A strong correlation was found between the protein aggregates in the cortex and the m parameter. Lenses without cataract are characterized using a classification and regression tree, by a mean brightness intensity ≤0.351, a variance of the B-scan brightness intensity ≤0.070, a velocity ≤1625 m/s, and an attenuation ≤0.415 dB/mm·MHz (sensitivity: 100% and specificity: 72.6%). To characterize different cataract degrees, the m parameter should be considered. Initial stages of cataract are characterized by a mean brightness intensity 〉0.351 and a variance of the m parameter 〉0.110. Advanced stages of cataract are characterized by a mean brightness intensity 〉0.351, a variance of the m parameter ≤0.110, and a mean m parameter 〉0.374. For initial and advanced stages of cataract, a sensitivity of 78.4% - nd a specificity of 86.5% are obtained.

Beschreibung: The substrate of potentially lethal cardiac arrhythmias often resides in the gray zone (GZ), a mixture of viable myocytes and collagen strands found between healthy myocardium and infarct core (IC). The specific aims of this paper are to demonstrate correspondence between regions delineated in T1 * (apparent T 1 ) maps and tissue characteristics seen in histopathology and to determine the MR imaging resolution needed to adequately identify GZ-associated substrate in chronic infarct. For this, a novel 3-D multicontrast late enhancement (MCLE) MR method was used to image ex vivo swine hearts with chronic infarction, at high resolution ( $0.6times 0.6times 1.25$ mm). Pixel-wise classified tissue maps were calculated using steady-state and T ${{bf _1}^{*}}$ images as input to a fuzzy-clustering algorithm. Quantitative histology based on collagen stains was performed in $n = 10$ selected slabs and showed very good correlations between histologically-determined areas of heterogeneous and dense fibrosis, and the corresponding GZ ( $R^{2} = 0.96$ ) and IC ( $R^{2} = 0.97$ ) in tissue classified maps. Furthermore, in $n = 24$ slabs, we performed volumetric measurements of GZ and IC, at the original and decreased image resolutions. Our results demonstrated that the IC volume remained relatively unchanged across all resolutions, whereas the GZ volume progressively increased with diminished image resolution, with changes reaching significance at $1times 1times 5$ mm resolution ( $p 〈0.05$ ) but not at $1times 1times 2.5$ mm, suggesting that this resolution may be sufficient to adequately identify the GZ from MCLE images, enabling an effective MR probing of remodeled myocardium in late infarct. Future work will focus on translating these findings to optimizing the current in vivo MCLE imaging of the GZ.

Beschreibung: A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an $mathcal {H}_{infty }$ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). A control-relevant model that is employed to design the robust controller is identified. The identification procedure is based on the distribution version of the UVA/Padova metabolic simulator using the simulation adult cohort. The SM prevents dangerous scenarios by acting upon a prediction of future glucose levels, and the IFL modifies the loop gain in order to reduce postprandial hypoglycemia risks. The procedure is tested on the complete in silico adult cohort of the UVA/Padova metabolic simulator, which has been accepted by the Food and Drug Administration (FDA) in lieu of animal trials.

Beschreibung: Provides a listing of current committee members and society officers.

Beschreibung: Brain research requires a standardized brain atlas to describe both the variance and invariance in brain anatomy and neuron connectivity. In this study, we propose a system to construct a standardized 3D Drosophila brain atlas by integrating labeled images from different preparations. The 3D fly brain atlas consists of standardized anatomical global and local reference models, e.g., the inner and external brain surfaces and the mushroom body. The averaged global and local reference models are generated by the model averaging procedure, and then the standard Drosophila brain atlas can be compiled by transferring the averaged neuropil models into the averaged brain surface models. The main contribution and novelty of our study is to determine the average 3D brain shape based on the isosurface suggested by the zero-crossings of a 3D accumulative signed distance map. Consequently, in contrast with previous approaches that also aim to construct a stereotypical brain model based on the probability map and a user-specified probability threshold, our method is more robust and thus capable to yield more objective and accurate results. Moreover, the obtained 3D average shape is useful for defining brain coordinate systems and will be able to provide boundary conditions for volume registration methods in the future. This method is distinguishable from those focusing on 2D + Z image volumes because its pipeline is designed to process 3D mesh surface models of Drosophila brains.

Beschreibung: Electroanatomical mapping (EAM) systems are commonly used in clinical practice for guiding catheter ablation treatments of common arrhythmias. In focal tachycardias, the ablation target is defined by locating the earliest activation area determined by the joint analysis of electrogram (EGM) signals at different sites. However, this is currently a manual time-consuming and experience-dependent task performed during the intervention and thus prone to stress-related errors. In this paper, we present an automatic delineation strategy that combines electrocardiogram (ECG) information with the wavelet decomposition of the EGM signal envelope to identify the onset of each EGM signal for activation mapping. Fourteen electroanatomical maps corresponding to ten patients suffering from non-tolerated premature ventricular contraction (PVC) beats and admitted for ablation procedure were used for evaluation. We compared the results obtained automatically with two types of manual annotations: one during the intervention by an expert technician (on-procedure) and other after the intervention (off-procedure), free from time and procedural constraints, by two other technicians. The automatic annotations show a significant correlation (0.95, p $〈$ 0.01) with the evaluation reference (off-procedure annotation sets combination) and has an error of 2.1 $pm$ 10.9 ms, around the order of magnitude of the on-procedure annotations error ( $-$ 2.6 $pm$ 6.8 ms). The results suggest that the proposed methodology could be incorporated into EAM systems to considerably reduce processing time during ablation interventions.

Beschreibung: Sleepiness and fatigue can reach particularly high levels during long-haul overnight flights. Under these conditions, voluntary or even involuntary sleep periods may occur, increasing the risk of accidents. The aim of this study was to assess the performance of an in-flight automatic detection system of low-vigilance states using a single electroencephalogram channel. Fourteen healthy pilots voluntarily wore a miniaturized brain electrical activity recording device during long-haul flights ( $10 pm 2.0$ h, Atlantic 2 and Falcon 50 M, French naval aviation). No subject was disturbed by the equipment. Seven pilots experienced at least a period of voluntary ( $26.8 pm 8.0$ min, $n = 4$ ) or involuntary sleep (N1 sleep stage, $26.6 pm 18.7$ s, $n = 7$ ) during the flight. Automatic classification (wake/sleep) by the algorithm was made for 10-s epochs (O1-M2 or C3-M2 channel), based on comparison of means to detect changes in α, β, and θ relative power, or ratio [( $alpha +theta$ )/β], or fuzzy logic fusion (α, β). Pertinence and prognostic of the algorithm were determined using epoch-by-epoch comparison with visual-scoring (two blinded readers, AASM rules). The best concordance between automatic detection and visual-scoring was observed within the O1-M2 channel, using the ratio [( $alpha +theta$ )/β] ( $98.3 pm 4.1%$ of good detection, $K = 0.94 pm 0.07$ , with a $0.04 pm 0.04$ false positive rate and a $0.87 pm 0.10$ true positive rate). Our results confirm the efficiency of a miniaturized single electroencephalographic channel recording device, associated with an automatic detection algorithm, in order to detect low-vigilance states during real flights.

Beschreibung: The design, implementation, and characterization of a microelectromechanical systems (MEMS) capacitive accelerometer-based middle ear microphone are presented in this paper. The microphone is intended for middle ear hearing aids as well as future fully implantable cochlear prosthesis. Human temporal bones acoustic response characterization results are used to derive the accelerometer design requirements. The prototype accelerometer is fabricated in a commercial silicon-on-insulator (SOI) MEMS process. The sensor occupies a sensing area of 1 mm × 1 mm with a chip area of 2 mm × 2.4 mm and is interfaced with a custom-designed low-noise electronic IC chip over a flexible substrate. The packaged sensor unit occupies an area of 2.5 mm × 6.2 mm with a weight of 25 mg. The sensor unit attached to umbo can detect a sound pressure level (SPL) of 60 dB at 500 Hz, 35 dB at 2 kHz, and 57 dB at 8 kHz. An improved sound detection limit of 34-dB SPL at 150 Hz and 24-dB SPL at 500 Hz can be expected by employing start-of-the-art MEMS fabrication technology, which results in an articulation index of approximately 0.76. Further micro/nanofabrication technology advancement is needed to enhance the microphone sensitivity for improved understanding of normal conversational speech.

Beschreibung: Breathwalk is a science of combining specific patterns of footsteps synchronized with the breathing. In this study, we developed a multimedia-assisted Breathwalk-aware system which detects user's walking and breathing conditions and provides appropriate multimedia guidance on the smartphone. Through the mobile device, the system enhances user's awareness of walking and breathing behaviors. As an example application in slow technology, the system could help meditator beginners learn “walking meditation,” a type of meditation which aims to be as slow as possible in taking pace, to synchronize footstep with breathing, and to land every footstep with toes first. In the pilot study, we developed a walking-aware system and evaluated whether multimedia-assisted mechanism is capable of enhancing beginner's walking awareness while walking meditation. Experimental results show that it could effectively assist beginners in slowing down the walking speed and decreasing incorrect footsteps. In the second experiment, we evaluated the Breathwalk-aware system to find a better feedback mechanism for learning the techniques of Breathwalk while walking meditation. The experimental results show that the visual-auditory mechanism is a better multimedia-assisted mechanism while walking meditation than visual mechanism and auditory mechanism.

Beschreibung: We have acquired 2-D and 3-D microwave tomographic images of the calcaneus bones of two patients to assess correlation of the microwave properties with X-ray density measures. The two volunteers were selected because each had one leg immobilized for at least six weeks during recovery from a lower leg injury. A soft-prior regularization technique was incorporated with the microwave imaging to quantitatively assess the bulk dielectric properties within the bone region. Good correlation was observed between both permittivity and conductivity and the computed tomography-derived density measures. These results represent the first clinical examples of microwave images of the calcaneus and some of the first 3-D tomographic images of any anatomical site in the living human.

Beschreibung: Diagnostic confirmation of cancer in solid organs is based on biopsy findings. In a standard 12-core prostate biopsy protocol, conventional biopsy needles sample only 0.95% (∼0.228 cm $^{3}$ ) of a typical 24-cm $^{3}$ prostate gland. The primary objective of this study was to enhance the sensitivity of standard biopsy protocol by gauging electrical properties of tissue simultaneously with tissue extraction for histopathology analysis. A conventional biopsy (Bx) needle was instrumented with an electrical impedance spectroscopy (EIS) sensor to interrogate the tissue volume surrounding the needle tip. The EIS-Bx device was evaluated in a series of saline bath and ex vivo porcine experiments. It was found to sense a volume of 0.286 cm $^{3}$ of tissue around the needle tip. EIS measurements were recorded from three ex vivo human prostates using the device, and the extracted biopsy cores were histologically assessed. Prostate conductivity σ ranged from 0.179 to 0.3310 S/m for benign tissues and 0.0746 to 0.0837 S/m for malignant tissues at frequencies ranging from 1 to 100 kHz. Relative permittivity ϵ $_{r}$ ranged from 2.10×10 $^{6}$ to 2.9 × 10 $^{4}$ for benign and 6.63×10 $^{5}$ to 5.3 × 10 $^{3}$ for cancer tissues over the same frequency range. Both are found to be significantly higher in normal prostate tissues than in malignant tissue $(p$ 〈 0.00001).

Beschreibung: Due to the growing shortage of donor livers, more patients are waiting for transplantation. Living donor liver transplantation may help expanding the donor pool, but is often confronted with the small-for-size syndrome. Since the hemodynamic effects of partial hepatectomy are not fully understood, we developed an electrical rat liver model to compare normal with resected liver hemodynamics. Detailed geometrical data and 3-D reconstructions of the liver vasculature of two rats were gathered by combining vascular corrosion casting, micro-CT scanning, and image processing. Data extrapolations allowed obtaining a total liver pressure- and flow-driven electrical analog. Subsequently, virtual resections led to 70%, 80%, or 90% partial hepatectomy models. Results demonstrated hyperperfusion effects such as portal hypertension and elevated lobe-specific portal venous flows (11, 12, and 24 mmHg, and 1.0–3.0, 1.8–3.5, and 7.4 ml/min for 70%, 80%, and 90% hepatectomy, respectively). Comparison of two 90% resection techniques demonstrated different total arterial flows (0.28 ml/min versus 0.61 ml/min), portal (24 mmHg versus 21 mmHg), and sinusoidal pressures (14 mmHg versus 9.5–12 mmHg), probably leading to better survival for lower portal and sinusoidal pressures. Toward the future, the models may be extrapolated to human livers and help us to optimize hepatectomy planning.

Beschreibung: This paper presents a useful technique for totally automatic detection of myocardial infarction from patients’ ECGs. Due to the large number of heartbeats constituting an ECG and the high cost of having all the heartbeats manually labeled, supervised learning techniques have achieved limited success in ECG classification. In this paper, we first discuss the rationale for applying multiple instance learning (MIL) to automated ECG classification and then propose a new MIL strategy called latent topic MIL, by which ECGs are mapped into a topic space defined by a number of topics identified over all the unlabeled training heartbeats and support vector machine is directly applied to the ECG-level topic vectors. Our experimental results on real ECG datasets from the PTB diagnostic database demonstrate that, compared with existing MIL and supervised learning algorithms, the proposed algorithm is able to automatically detect ECGs with myocardial ischemia without labeling any heartbeats. Moreover, it improves classification quality in terms of both sensitivity and specificity.

Beschreibung: In high-field magnetic resonance imaging (MRI) systems, $B_0$ fields of 7 and 9.4 T, the RF field shows greater inhomogeneity compared to clinical MRI systems with $B_0$ fields of 1.5 and 3.0 T. In multichannel RF coils, the magnitude and phase of the input to each coil element can be controlled independently to reduce the nonuniformity of the RF field. The convex optimization technique has been used to obtain the optimum excitation parameters with iterative solutions for homogeneity in a selected region of interest. The pseudoinverse method has also been used to find a solution. The simulation results for 9.4- and 7-T MRI systems are discussed in detail for the head model. Variation of the simulation results in a 9.4-T system with the number of RF coil elements for different positions of the regions of interest in a spherical phantom are also discussed. Experimental results were obtained in a phantom in the 9.4-T system and are compared to the simulation results and the specific absorption rate has been evaluated.

Beschreibung: Motivated by the goals of automatically extracting vessel segments and constructing retinal vascular trees with anatomical realism, this paper presents and analyses an algorithm that combines vessel segmentation and grouping of the extracted vessel segments. The proposed method aims to restore the topology of the vascular trees with anatomical realism for clinical studies and diagnosis of retinal vascular diseases, which manifest abnormalities in either venous and/or arterial vascular systems. Vessel segments are grouped using extended Kalman filter which takes into account continuities in curvature, width, and intensity changes at the bifurcation or crossover point. At a junction, the proposed method applies the minimum-cost matching algorithm to resolve the conflict in grouping due to error in tracing. The system was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images. The dataset contained a mixture of normal and pathological images. In addition, six pathological fluorescein angiogram sequences were also included in this study. The results were compared against the groundtruth images provided by a physician, achieving average success rates of 88.79% and 90.09%, respectively.

Beschreibung: Model averaging is a widely used technique in biomedical applications. Two established model averaging methods, iterative shape averaging (ISA) method and virtual insect brain (VIB) method, have been applied to several organisms to generate average representations of their brain surfaces. However, without sufficient samples, some features of the average Drosophila brain surface obtained using the above methods may disappear or become distorted. To overcome this problem, we propose a Bézier-tube-based surface model averaging strategy. The proposed method first compensates for disparities in position, orientation, and dimension of input surfaces, and then evaluates the average surface by performing shape-based interpolation. Structural features with larger individual disparities are simplified with half-ellipse-shaped Bézier tubes, and are unified according to these tubes to avoid distortion during the averaging process. Experimental results show that the average model yielded by our method could preserve fine features and avoid structural distortions even if only a limit amount of input samples are used. Finally, we qualitatively compare our results with those obtained by ISA and VIB methods by measuring the surface-to-surface distances between input surfaces and the averaged ones. The comparisons show that the proposed method could generate a more representative average surface than both ISA and VIB methods.

Beschreibung: The aim of this study is to investigate muscular fatigue and to propose a new fatigue index based on the continuous wavelet transform (CWT) which is compared to the standard fatigue indexes from literature. Fatigue indexes are all based on the electrical activity of muscles [electromyogram (EMG)] acquired during an electrically stimulated contraction thanks to two modules (electromyostimulation + electromyography recording) that can analyze EMG signals in real time during electromyostimulation. The extracted parameters are compared with each other and their sensitivity to noise is studied. The effect of truncation of M waves is then investigated, enlightening the robustness of the index obtained using CWT.

Beschreibung: Patient-specific mathematical models of respiratory mechanics can offer substantial insight into patient state and pulmonary dynamics that are not directly measurable. Thus, they offer significant potential to evaluate and guide patient-specific lung protective ventilator strategies for acute respiratory distress syndrome (ARDS) patients. To assure bedside applicability, the model must be computationally efficient and identifiable from the limited available data, while also capturing dominant dynamics and trends observed in ARDS patients. In this study, an existing static recruitment model is enhanced by considering alveolar distension and implemented in a novel time-continuous dynamic respiratory mechanics model. The model was tested for structural identifiability and a hierarchical gradient descent approach was used to fit the model to low-flow test responses of 12 ARDS patients. Finally, a comprehensive practical identifiability analysis was performed to evaluate the impact of data quality on the model parameters. Identified parameter values were physiologically plausible and very accurately reproduced the measured pressure responses. Structural identifiability of the model was proven, but practical identifiability analysis of the results showed a lack of convexity on the error surface indicating that successful parameter identification is currently not assured in all test sets. Overall, the model presented is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show the ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics currently cannot be directly measured or established without such a validated model.

Beschreibung: In this paper, a new conductive polymer foam-surfaced electrode was proposed for use as a capacitive EEG electrode for nonintrusive EEG measurements in out-of-hospital environments. The current capacitive electrode has a rigid surface that produces an undefined contact area due to its stiffness, which renders it unable to conform to head curvature and locally isolates hairs between the electrode surface and scalp skin, making EEG measurement through hair difficult. In order to overcome this issue, a conductive polymer foam was applied to the capacitive electrode surface to provide a cushioning effect. This enabled EEG measurement through hair without any conductive contact with bare scalp skin. Experimental results showed that the new electrode provided lower electrode–skin impedance and higher voltage gains, signal-to-noise ratios, signal-to-error ratios, and correlation coefficients between EEGs measured by capacitive and conventional resistive methods compared to a conventional capacitive electrode. In addition, the new electrode could measure EEG signals, while the conventional capacitive electrode could not. We expect that the new electrode presented here can be easily installed in a hat or helmet to create a nonintrusive wearable EEG apparatus that does not make users look strange for real-world EEG applications.

Beschreibung: Understanding the skin's material properties and natural motion is critical to a myriad of applications from tissue engineering to spacesuits. While there is an extensive understanding of human skin properties based on active tensile testing, both in vitro and in vivo, there is a little current knowledge of the strains experienced by skin during natural movements. Using a motion capture system, we have developed a new technique to measure skin movement and strain around the knee during a squatting motion. With these new data, we are also able to calculate the local direction of lines of nonextension, or contours of the skin that remain a constant length during motion, lines of minimum extension, and lines of minimum compression.

Beschreibung: A partially perturbed particle swarm optimization (PPSO) has been proposed for identifying the parameters of the Beeler–Reuter (BR) equation from action potential data. In the PPSO algorithm, the 63 BR equation parameters are divided into groups, and parameter patterns are made from the combination of the groups. PPSO enhances the capability of conventional particle swarm optimization (CPSO) by partially perturbing the coordinates of the globally best particle with the patterns when the searching process is locally confined. “Experimental data” were produced for cardiac myocytes simulated by the BR equation and the equation of Luo and Rudy (1991), and were used to test the algorithm of PPSO. The test results show that PPSO was able to identify the parameters of the BR equation effectively for different cardiac myocytes, while still retaining the conceptual simplicity and easy implementation of CPSO.

Beschreibung: Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80–145 mg/dL, using insulin and nutrition control for 1–3 h interventions. Insulin changes are limited to +3U/h and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run on using clinical data from 371 patients (39841 h) from the Specialized Relative Insulin and Nutrition Tables (SPRINT) cohort. Cohort and per-patient results are compared to clinical SPRINT data, and virtual trials of three published protocols. Performance was measured as time within glycemic bands, and safety by patients with severe (BG 〈 40 mg/dL) and mild (%BG 〈 72 mg/dL) hypoglycemia. Pilot trial results from the first ten patients (1486 h) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. AGC was tighter than both SPRINT clinical data and in-silico comparison protocols, with 91% BG within the specified target (80–145 mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.2/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe AGC with significant reductions in hypoglycemia and clinical effort due - o stochastic forecasting of patient variation—a unique risk-based approach. Initial pilot trials validate the in-silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.

Beschreibung: Epilepsy is a neurological disorder characterized by sudden, often unexpected transitions from normal to pathological behavioral states called epileptic seizures. Some of these seizures are accompanied by uncontrolled, often rhythmic movements of body parts when seizure activity propagates to brain areas responsible for the initiation and control of movement. The dynamics of these transitions is, in general, unknown. As a consequence, individuals have to be monitored for long periods in order to obtain sufficient data for adequate diagnosis and to plan therapeutic strategy. Some people may require long-term care in special units to allow for timely intervention in case seizures get out of control. Our goal is to present a method by which a subset of motor seizures can be detected using only remote sensing devices (i.e., not in contact with the subject) such as video cameras. These major motor seizures (MMS) consist of clonic movements and are often precursors of generalized tonic–clonic (convulsive) seizures, sometimes leading to a condition known as status epilepticus, which is an acute life-threatening event. We propose an algorithm based on optical flow, extraction of global group transformation velocities, and band-pass temporal filtering to identify occurrence of clonic movements in video sequences. We show that for a validation set of 72 prerecorded epileptic seizures in 50 people, our method is highly sensitive and specific in detecting video segments containing MMS with clonic movements.

Beschreibung: Face plastic surgery (PS) plays a major role in today medicine. Both for reconstructive and cosmetic surgery, achieving harmony of facial features is an important, if not the major goal. Several systems have been proposed for presenting to patient and surgeon possible outcomes of the surgical procedure. In this paper, we present a new 3-D system able to automatically suggest, for selected facial features as nose, chin, etc., shapes that aesthetically match the patient's face. The basic idea is suggesting shape changes aimed to approach similar but more harmonious faces. To this goal, our system compares the 3-D scan of the patient with a database of scans of harmonious faces, excluding the feature to be corrected. Then, the corresponding features of the $k$ most similar harmonious faces, as well as their average, are suitably pasted onto the patient's face, producing k+1 aesthetically effective surgery simulations. The system has been fully implemented and tested. To demonstrate the system, a 3-D database of harmonious faces has been collected and a number of PS treatments have been simulated. The ratings of the outcomes of the simulations, provided by panels of human judges, show that the system and the underlying idea are effective.

Beschreibung: Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Beschreibung: Tetrapolar bioimpedance measurements on subjects have long been suspected of being affected by stray capacitance between the subjects’ body and ground. This paper provides a circuit model to analyze that effect in the frequency range from 100 Hz to 1 MHz in order to identify the relevant parameters when impedance is measured by applying a voltage and measuring both the resulting current and the potential difference between two points on the surface of the volume conductor. The proposed model includes the impedance of each electrode and the input impedance of the differential voltage amplifier. When common values for the circuit parameters are assumed, the simplified model predicts: 1) a frequency-independent gain (scale factor) error; 2) inductive artifacts, that is, the measured impedance increases with increasing frequency and may include positive angle phases; and 3) resonance that can affect well below 1 MHz. In addition to the stray capacitance to ground, relevant parameters that determine those errors are the capacitance of the “low-current” electrode and the input capacitance of the differential voltage amplifier. Experimental results confirm those theoretical predictions and show effects from several additional resonances above 1 MHz that also depend on body capacitance to ground.

Beschreibung: We introduce a method to automatically extract spike features of the AIDS virus imaged through an electron microscope. The AIDS virus spike is the primary target of drug design as it is directly involved in infecting host cells. Our method detects the location of these spikes and extracts a subvolume enclosing the spike. We have achieved a sensitivity of 80% for our best operating range. The extracted spikes are further aligned and combined to build a 4-D statistical shape model, where each voxel in the shape model is assigned a probability density function. Our method is the first fully automated technique that can extract subvolumes of the AIDS virus spike and be used to build a statistical model without the need for any user supervision. We envision that this new tool will significantly enhance the overall process of shape analysis of the AIDS virus spike imaged through the electron microscope. Accurate models of the virus spike will help in the development of better drug design strategies.

Beschreibung: Electromagnetic absorption and subsequent heating of nanoparticle solutions and simple NaCl ionic solutions is examined for biomedical applications in the radiofrequency range at 13.56 MHz. It is shown via both theory and experiment that for in vitro measurements the shape of the solution container plays a major role in absorption and heating.

Beschreibung: Image-guided needle placement, including ultrasound (US)-guided techniques, have become commonplace in modern medical diagnosis and therapy. To ensure that the next generations of physicians are competent using this technology, efficient and effective educational programs need to be developed. This paper presents the Perk Tutor: a configurable, open-source training platform for US-guided needle insertions. The Perk Tutor was successfully tested in three different configurations to demonstrate its adaptability to different procedures and learning objectives. 1) The Targeting Tutor, designed to develop US-guided needle targeting skills, 2) the Lumbar Tutor, designed for practicing US-guided lumbar spinal procedures, and (3) the Prostate Biopsy Tutor, configured for US-guided prostate biopsies. The Perk Tutor provides the trainee with quantitative feedback on progress toward the specific learning objectives of each configuration. Configurations were implemented through simple rearrangement of hardware and software components, attesting to the modularity and ease of configuration. The Perk Tutor is provided as a free resource to enable research and development of educational programs for US-guided intervention.

Beschreibung: Histological tissue sections provide rich information and continue to be the gold standard for the assessment of tissue neoplasm. However, there are a significant amount of technical and biological variations that impede analysis of large histological datasets. In this paper, we have proposed a novel approach for nuclear segmentation in tumor histology sections, which addresses the problem of technical and biological variations by incorporating information from both manually annotated reference patches and the original image. Subsequently, the solution is formulated within a multireference level set framework. This approach has been validated on manually annotated samples and then applied to the TCGA glioblastoma multiforme (GBM) dataset consisting of 440 whole mount tissue sections scanned with either a 20 $times$ or 40 $times$ objective, in which, each tissue section varies in size from 40k $times$ 40k pixels to 100k $times$ 100k pixels. Experimental results show a superior performance of the proposed method in comparison with present state of art techniques.

Beschreibung: Regulation of intracranial pressure (ICP) is vital to proper brain function. Pathologic conditions such as traumatic brain injury and hydrocephalus can cause lethal changes in ICP through an imbalance of fluid passage into and out of the craniospinal space. The relationship between craniospinal volume and pressure determines to a large extent whether such imbalance can be compensated or if it will lead to neuronal damage. Phantom models are predisposed for the evaluation of medical procedures and devices that alter volume in the spinal or cranial space. However, current phantoms have substantial limitations in the reproduction of craniospinal pressure–volume relationships, which need to be overcome prior to their deployment outside the basic research setting. We present herein a novel feedback controlled phantom for the reproduction of any physiologic or pathologic pressure–volume relation. We compare its performance to those of existing passive methods, showing that it follows reference curves more precisely during both infusion of large volumes and fast oscillatory volume changes.

Beschreibung: With proper source spacing, low loss left-handed metamaterial (LHM) lens should be useful for hyperthermia treatment of large area tumors. With a flat LHM lens applicator, conformal hyperthermia can be performed by joint heating of multiple microwave sources (antennas). In the hyperthermia, we restrict distance of two neighboring sources within a critical source interval, arrange the sources in a specific array of general shape in accord with the tumor, and adjust the source-to-lens distance to acquire desired inclination of the heating zone for better fit to tumor region. It is shown that inclination can also be adjusted by the phases of microwave sources. A maneuverable LHM-based hyperthermia scheme is thus proposed to generate a relatively large and even tilted heating pattern in tissue.

Beschreibung: Magnetic resonance images of the tongue have been used in both clinical studies and scientific research to reveal tongue structure. In order to extract different features of the tongue and its relation to the vocal tract, it is beneficial to acquire three orthogonal image volumes—e.g., axial, sagittal, and coronal volumes. In order to maintain both low noise and high visual detail and minimize the blurred effect due to involuntary motion artifacts, each set of images is acquired with an in-plane resolution that is much better than the through-plane resolution. As a result, any one dataset, by itself, is not ideal for automatic volumetric analyses such as segmentation, registration, and atlas building or even for visualization when oblique slices are required. This paper presents a method of superresolution volume reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image volumes. The method uses preprocessing steps that include registration and intensity matching and a data combination approach with the edge-preserving property carried out by Markov random field optimization. The performance of the proposed method was demonstrated on 15 clinical datasets, preserving anatomical details and yielding superior results when compared with different reconstruction methods as visually and quantitatively assessed.

Beschreibung: Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems—a laser scanner and the current standard electromagnetic digitization system (Polhemus)—using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system.

Beschreibung: The ability to accurately locate a polyp found on computed tomographic colonography (CTC) at subsequent optical colonoscopy (OC) is an important task in colorectal cancer screening. We present a method to more accurately match polyp locations at CTC and OC. A colonoscope was modeled as a flexible tube with negligible stretch and minimal strain. The path of the colonoscope was estimated using a minimal-energy curve method. The energy function was defined and optimized by a subdivision scheme. The prediction of polyp locations at OC from CTC was converted to an optimization problem. The prediction performance was evaluated on 134 polyps by comparing the predicted with the true polyp locations at OC. The method can accurately predict polyp locations at OC to within ±0.5 colonoscope mark (5 cm) for more than 58% of polyps and to within ±1 colonoscope mark (10 cm) for more than 96% of polyps, significantly improving upon previously published methods. This method can be easily incorporated into routine OC practice and allow the colonoscopist to begin the examination by targeting locations of potential polyps found at CTC.

Beschreibung: System identification of physiological systems poses unique challenges, especially when the structure of the system under study is uncertain. Nonparametric techniques can be useful for identifying system structure, but these typically assume stationarity and require large amounts of data. Both of these requirements are often not easily obtained in the study of physiological systems. Ensemble methods for time-varying nonparametric estimation have been developed to address the issue of stationarity, but these require an amount of data that can be prohibitive for many experimental systems. To address this issue, we developed a novel algorithm that uses multiple short data segments. Using simulation studies, we showed that this algorithm produces system estimates with lower variability than previous methods when limited data are present. Furthermore, we showed that the new algorithm generates time-varying system estimates with lower total error than an ensemble method. Thus, this algorithm is well suited for the identification of physiological systems that vary with time or from which only short segments of stationary data can be collected.

Beschreibung: Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects’ electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time–frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao–Atlas–Marks distribution and Hilbert–Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, $k$ -nearest neighbors $(k$ -NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76)%} was achieved using $k$ -NN and HHS-based feature vectors ( FVs) representing a bilateral average activity, referred to a resting period, in $beta$ (13–30 Hz) and $gamma$ (30–49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this study provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.

Beschreibung: Spectral CT has proven an important development in biomedical imaging, and there have been several publications in the past years demonstrating its merits in pre-clinical and clinical applications. In 2012, Xu reported that near-term implementation of spectral micro-CT could be enhanced by a hybrid architecture: a narrow-beam spectral “interior” imaging chain integrated with a traditional wide-beam “global” imaging chain. This hybrid integration coupled with compressive sensing (CS)-based interior tomography demonstrated promising results for improved contrast resolution, and decreased system cost and radiation dose. The motivation for the current study is implementation and evaluation of the hybrid architecture with a first-of-its-kind hybrid spectral micro-CT system. Preliminary results confirm improvements in both contrast and spatial resolution. This technology is shown to merit further investigation and potential application in future spectral CT scanner design.

Beschreibung: Tumors are typically analyzed as a single unit, despite their biologically heterogeneous nature. This limits correlations that can be drawn between regional variation and treatment outcome. Furthermore, despite the availability of high resolution 3-D medical imaging techniques, local outcomes, (e.g., tumor growth), are not easily measured. This paper proposes a method that uses streamlines to divide a 3-D region of interest (e.g., tumor) into units where local properties can be measured over the paths of growth. The parameters such as directional length and mean intensity can be measured locally at sequential time points and then compared. The method is evaluated on synthetic objects, simulated tumors, and medical images of brain tumors. The evaluations suggest that the method is suitable for mapping amorphous dynamic objects.

Beschreibung: Monitoring heart activity from electrocardiograms (ECG) is crucial to avoid unnecessary fatalities; therefore, detection of QRS complex is fundamental to automated ECG monitoring. Continuous, portable 24/7 ECG monitoring requires wireless technology with constraints on power, bandwidth, area, and resolution. In order to provide continuous remote monitoring of patients and fast transmission of data to medical personnel for instantaneous intervention, we propose a methodology that converts analog inputs into pulses for ultralow power implementation. The signal encoding scheme is the time-based integrate and fire (IF) sampler from which a set of signal descriptors in the pulse domain are proposed. Furthermore, a logical decision rule for QRS detection based on morphological checking is derived. The proposed decision logic depends exclusively on relational and logical operators resulting in ultrafast recognition and can be implemented using combinatorial logic hardware to guarantee power consumption orders of magnitude lower than any microprocessor device. The algorithm was evaluated using the MIT-BIH arrhythmia database and results show that our algorithm performance is comparable to the state-of-the art software-based detection.

Beschreibung: In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DOFs) (wrist flexion–extension, abduction–adduction, and pronation–supination) using mirrored bilateral contractions. In the resisted unconstrained experiment, some resistance to movement was provided using flexible wrist braces in order to increase the required level of muscle activation. The force, in constrained experiments, and position, in unconstrained and resisted unconstrained experiments, were measured. The three protocols were compared off-line using estimation accuracies $(R^{2})$ and online using a real-time computer-based target acquisition test. The constrained control paradigm outperformed the unconstrained method in the abduction–adduction DOF $(R^{2}_{rm constrained}$ = 90.8 ± 0.6, $R^{2}_{rm unconstrained}$ = 85.6 ± 1.6) and pronation–supination DOF ( $R^{2}_{rm constrained}$ = 88.5 ± 0.9, $R^{2}_{rm unconstrained}$ = 82.3 ± 1.6), but no significant difference was found in the flexion–extension DOF. The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time- and path efficiency. The constrained method results, however, were not significantly different than those of the resisted unconstrained method (with braces) in both off-line and real-time tests. This suggests that the quality of control using constrained and unconstrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation.

Beschreibung: This study proposes a novel hybrid brain–computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.

Beschreibung: Wearable and implantable wireless communication devices have in recent years gained increasing attention for medical diagnostics and therapeutics. In particular, wireless capsule endoscopy has become a popular method to visualize and diagnose the human gastrointestinal tract. Estimating the exact position of the capsule when each image is taken is a very critical issue in capsule endoscopy. Several approaches have been developed by researchers to estimate the capsule location. However, some unique challenges exist for in-body localization, such as the severe multipath issue caused by the boundaries of different organs, inconsistency of signal propagation velocity and path loss parameters inside the human body, and the regulatory restrictions on using high-bandwidth or high-power signals. In this paper, we propose a novel localization method based on spatial sparsity. We directly estimate the location of the capsule without going through the usual intermediate stage of first estimating time-of-arrival or received-signal strength, and then a second stage of estimating the location. We demonstrate the accuracy of the proposed method through extensive Monte Carlo simulations for radio frequency emission signals within the required power and bandwidth range. The results show that the proposed method is effective and accurate, even in massive multipath conditions.

Beschreibung: In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This reduction results in decreased resolution and accuracy of the estimated source configuration. This paper develops and tests a new multistage adaptive processing technique based on the minimum variance beamformer for brain source localization that has been previously used in the radar statistical signal processing context. This processing, referred to as the fast fully adaptive (FFA) approach, can significantly reduce the required sample support, while still preserving all available DoFs. To demonstrate the performance of the FFA approach in the limited data scenario, simulation and experimental results are compared with two previous beamforming approaches; i.e., the fully adaptive minimum variance beamforming method and the beamspace beamforming method. Both simulation and experimental results demonstrate that the FFA method can localize all types of brain activity more accurately than the other approaches with limited data.

Beschreibung: Accurate estimation of daily total energy expenditure (EE) is a prerequisite for assisted weight management and assessing certain health conditions. The use of wearable sensors for predicting free-living EE is challenged by consistent sensor placement, user compliance, and estimation methods used. This paper examines whether a single ear-worn accelerometer can be used for EE estimation under free-living conditions. An EE prediction model was first derived and validated in a controlled setting using healthy subjects involving different physical activities. Ten different activities were assessed showing a tenfold cross validation error of 0.24. Furthermore, the EE prediction model shows a mean absolute deviation below 1.2 metabolic equivalent of tasks. The same model was applied to a free-living setting with a different population for further validation. The results were compared against those derived from doubly labeled water. In free-living settings, the predicted daily EE has a correlation of $hbox{0.74}, p = hbox{0.008}$ , and a MAD of $hbox{27}, hbox{kcal/day}$ . These results demonstrate that laboratory-derived prediction models can be used to predict EE under free-living conditions.

Beschreibung: In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain–computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; $p = 0.007$ ) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; $p = 0.016$ ). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; $p = 0.013$ ), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.