Showing papers in "IEEE Transactions on Biomedical Engineering in 2014"

Unobtrusive Sensing and Wearable Devices for Health Informatics

Abstract: The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

Breast Cancer Histopathology Image Analysis: A Review

Abstract: This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.

A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

Abstract: Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.

Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

Abstract: Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.

PLUS: open-source toolkit for ultrasound-guided intervention systems.

Abstract: A variety of advanced image analysis methods have been under the development for ultrasound-guided interventions. Unfortunately, the transition from an image analysis algorithm to clinical feasibility trials as part of an intervention system requires integration of many components, such as imaging and tracking devices, data processing algorithms, and visualization software. The objective of our paper is to provide a freely available open-source software platform-PLUS: Public software Library for Ultrasound-to facilitate rapid prototyping of ultrasound-guided intervention systems for translational clinical research. PLUS provides a variety of methods for interventional tool pose and ultrasound image acquisition from a wide range of tracking and imaging devices, spatial and temporal calibration, volume reconstruction, simulated image generation, and recording and live streaming of the acquired data. This paper introduces PLUS, explains its functionality and architecture, and presents typical uses and performance in ultrasound-guided intervention systems. PLUS fulfills the essential requirements for the development of ultrasound-guided intervention systems and it aspires to become a widely used translational research prototyping platform. PLUS is freely available as open source software under BSD license and can be downloaded from http://www.plustoolkit.org.

Visual and Auditory Brain–Computer Interfaces

Abstract: Over the past several decades, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, building robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual and auditory brain responses remains a challenging problem. In this paper, we review the current state and future challenges of visual and auditory BCI systems. First, we describe a new taxonomy based on the multiple access methods used in telecommunication systems. Then, we discuss the challenges of translating current technology into real-life practices and outline potential avenues to address them. Specifically, this review aims to provide useful guidelines for exploring new paradigms and methodologies to improve the current visual and auditory BCI technology.

Microwave-Based Stroke Diagnosis Making Global Prehospital Thrombolytic Treatment Possible

Abstract: Here, we present two different brain diagnostic devices based on microwave technology and the associated two first proof-of-principle measurements that show that the systems can differentiate hemorrhagic from ischemic stroke in acute stroke patients, as well as differentiate hemorrhagic patients from healthy volunteers. The system was based on microwave scattering measurements with an antenna system worn on the head. Measurement data were analyzed with a machine-learning algorithm that is based on training using data from patients with a known condition. Computer tomography images were used as reference. The detection methodology was evaluated with the leave-one-out validation method combined with a Monte Carlo-based bootstrap step. The clinical motivation for this project is that ischemic stroke patients may receive acute thrombolytic treatment at hospitals, dramatically reducing or abolishing symptoms. A microwave system is suitable for prehospital use, and therefore has the potential to allow significantly earlier diagnosis and treatment than today.

Retinal Prosthesis

Abstract: Retinal prosthesis has been translated from the laboratory to the clinic over the past two decades. Currently, two devices have regulatory approval for the treatment of retinitis pigmentosa. These devices provide partial sight restoration and patients use this improved vision in their everyday lives. Improved mobility and object detection are some of the more notable findings from the clinical trials. However, significant vision restoration will require both better technology and improved understanding of the interaction between electrical stimulation and the retina. This paper reviews the recent clinical trials and highlights technology breakthroughs that will contribute to next generation of retinal prostheses.

Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer

Abstract: Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, with subject-to-subject variability. This paper presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The algorithm utilizes data from a single, waist-mounted triaxial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby reducing user training. Feature selection is performed using Relief-F and sequential forward floating search (SFFS) from a range of previously published features, as well as new features introduced in this paper. Relevant and robust features that are insensitive to the positioning of accelerometer around the waist are selected. SFFS selected almost half the number of features in comparison to Relief-F and provided higher accuracy than Relief-F. Activity classification is performed using Naive Bayes and $k$ -nearest neighbor $(k$ -NN) and the results are compared. Activity recognition results on seven subjects with leave-one-person-out error estimates show an overall accuracy of about 98% for both the classifiers. Accuracy for each of the individual activity is also more than 95%.

Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera

Abstract: Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing. However, traditional RGB cameras have limited frequency resolution. We present results of PPG measurements from a novel five band camera and show that alternate frequency bands, in particular an orange band, allowed physiological measurements much more highly correlated with an FDA approved contact PPG sensor. In a study with participants (n = 10) at rest and under stress, correlations of over 0.92 (p 0.01) were obtained for heart rate, breathing rate, and heart rate variability measurements. In addition, the remotely measured heart rate variability spectrograms closely matched those from the contact approach. The best results were obtained using a combination of cyan, green, and orange (CGO) bands; incorporating red and blue channel observations did not improve performance. In short, RGB is not optimal for this problem: CGO is better. Incorporating alternative color channel sensors should not increase the cost of such cameras dramatically.

Biopsy using a Magnetic Capsule Endoscope Carrying, Releasing, and Retrieving Untethered Microgrippers

Abstract: This paper proposes a new wireless biopsy method where a magnetically actuated untethered soft capsule endoscope carries and releases a large number of thermo-sensitive, untethered microgrippers (μ-grippers) at a desired location inside the stomach and retrieves them after they self-fold and grab tissue samples. We describe the working principles and analytical models for the μ-gripper release and retrieval mechanisms, and evaluate the proposed biopsy method in ex vivo experiments. This hierarchical approach combining the advanced navigation skills of centimeter-scaled untethered magnetic capsule endoscopes with highly parallel, autonomous, submillimeter scale tissue sampling μ-grippers offers a multifunctional strategy for gastrointestinal capsule biopsy.

A Review of Multitaper Spectral Analysis

Abstract: Nonparametric spectral estimation is a widely used technique in many applications ranging from radar and seismic data analysis to electroencephalography (EEG) and speech processing Among the techniques that are used to estimate the spectral representation of a system based on finite observations, multitaper spectral estimation has many important optimality properties, but is not as widely used as it possibly could be We give a brief overview of the standard nonparametric spectral estimation theory and the multitaper spectral estimation, and give two examples from EEG analyses of anesthesia and sleep

Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach

Abstract: Correct detection and classification of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is of pivotal importance for an automatic external defibrillator and patient monitoring. In this paper, a VF/VT classification algorithm using a machine learning method, a support vector machine, is proposed. A total of 14 metrics were extracted from a specific window length of the electrocardiogram (ECG). A genetic algorithm was then used to select the optimal variable combinations. Three annotated public domain ECG databases (the American Heart Association Database, the Creighton University Ventricular Tachyarrhythmia Database, and the MIT-BIH Malignant Ventricular Arrhythmia Database) were used as training, test, and validation datasets. Different window sizes, varying from 1 to 10 s were tested. An accuracy (Ac) of 98.1%, sensitivity (Se) of 98.4%, and specificity (Sp) of 98.0% were obtained on the in-sample training data with 5 s-window size and two selected metrics. On the out-of-sample validation data, an Ac of 96.3% ± 3.4%, Se of 96.2% ± 2.7%, and Sp of 96.2% ± 4.6% were obtained by fivefold cross validation. The results surpass those of current reported methods.

Small-Animal Whole-Body Photoacoustic Tomography: A Review

Abstract: With the wide use of small animals for biomedical studies, in vivo small-animal whole-body imaging plays an increasingly important role. Photoacoustic tomography (PAT) is an emerging whole-body imaging modality that shows great potential for preclinical research. As a hybrid technique, PAT is based on the acoustic detection of optical absorption from either endogenous tissue chromophores, such as oxyhemoglobin and deoxyhemoglobin, or exogenous contrast agents. Because ultrasound scatters much less than light in tissue, PAT generates high-resolution images in both the optical ballistic and diffusive regimes. Using near-infrared light, which has relatively low blood absorption, PAT can image through the whole body of small animals with acoustically defined spatial resolution. Anatomical and vascular structures are imaged with endogenous hemoglobin contrast, while functional and molecular images are enabled by the wide choice of exogenous optical contrasts. This paper reviews the rapidly growing field of small-animal whole-body PAT and highlights studies done in the past decade.

Augmented Reality Navigation With Automatic Marker-Free Image Registration Using 3-D Image Overlay for Dental Surgery

Abstract: Computer-assisted oral and maxillofacial surgery (OMS) has been rapidly evolving since the last decade. State-of-the-art surgical navigation in OMS still suffers from bulky tracking sensors, troublesome image registration procedures, patient movement, loss of depth perception in visual guidance, and low navigation accuracy. We present an augmented reality navigation system with automatic marker-free image registration using 3-D image overlay and stereo tracking for dental surgery. A customized stereo camera is designed to track both the patient and instrument. Image registration is performed by patient tracking and real-time 3-D contour matching, without requiring any fiducial and reference markers. Real-time autostereoscopic 3-D imaging is implemented with the help of a consumer-level graphics processing unit. The resulting 3-D image of the patient's anatomy is overlaid on the surgical site by a half-silvered mirror using image registration and IP-camera registration to guide the surgeon by exposing hidden critical structures. The 3-D image of the surgical instrument is also overlaid over the real one for an augmented display. The 3-D images present both stereo and motion parallax from which depth perception can be obtained. Experiments were performed to evaluate various aspects of the system; the overall image overlay error of the proposed system was 0.71 mm.

Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification

Abstract: This paper presents new structural statistical matrices which are gray level size zone matrix (SZM) texture descriptor variants. The SZM is based on the cooccurrences of size/intensity of each flat zone (connected pixels with the same gray level). The first improvement increases the information processed by merging multiple gray-level quantizations and reduces the required parameter numbers. New improved descriptors were especially designed for supervised cell texture classification. They are illustrated thanks to two different databases built from quantitative cell biology. The second alternative characterizes the DNA organization during the mitosis, according to zone intensities radial distribution. The third variant is a matrix structure generalization for the fibrous texture analysis, by changing the intensity/size pair into the length/orientation pair of each region.

Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

Abstract: 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.

Noncontact Accurate Measurement of Cardiopulmonary Activity Using a Compact Quadrature Doppler Radar Sensor

Abstract: The designed sensor enables accurate reconstruction of chest-wall movement caused by cardiopulmonary activities, and the algorithm enables estimation of respiration, heartbeat rate, and some indicators of heart rate variability (HRV). In particular, quadrature receiver and arctangent demodulation with calibration are introduced for high linearity representation of chest displacement; 24-bit ADCs with oversampling are adopted for radar baseband acquisition to achieve a high signal resolution; continuous-wavelet filter and ensemble empirical mode decomposition (EEMD) based algorithm are applied for cardio/pulmonary signal recovery and separation so that accurate beat-to-beat interval can be acquired in time domain for HRV analysis. In addition, the wireless sensor is realized and integrated on a printed circuit board compactly. The developed sensor system is successfully tested on both simulated target and human subjects. In simulated target experiments, the baseband signal-to-noise ratio (SNR) is 73.27 dB, high enough for heartbeat detection. The demodulated signal has 0.35% mean squared error, indicating high demodulation linearity. In human subject experiments, the relative error of extracted beat-to-beat intervals ranges from 2.53% to 4.83% compared with electrocardiography (ECG) R-R peak intervals. The sensor provides an accurate analysis for heart rate with the accuracy of 100% for p = 2% and higher than 97% for p = 1%.

Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines

Abstract: Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each parameter individually. In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously proposed ECG parameters by using support vector machines classifiers. A total of 13 parameters were computed accounting for temporal (morphological), spectral, and complexity features of the ECG signal. A filter-type feature selection (FS) procedure was proposed to analyze the relevance of the computed parameters and how they affect the detection performance. The proposed methodology was evaluated in two different binary detection scenarios: shockable (FV plus VT) versus nonshockable arrhythmias, and VF versus nonVF rhythms, using the information contained in the medical imaging technology database, the Creighton University ventricular tachycardia database, and the ventricular arrhythmia database. sensitivity (SE) and specificity (SP) analysis on the out of sample test data showed values of SE=95%, SP=99%, and SE=92% , SP=97% in the case of shockable and VF scenarios, respectively. Our algorithm was benchmarked against individual detection schemes, significantly improving their performance. Our results demonstrate that the combination of ECG parameters using statistical learning algorithms improves the efficiency for the detection of life-threatening arrhythmias.

Human Brain Distinctiveness Based on EEG Spectral Coherence Connectivity

Abstract: The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

Brain tumor segmentation based on local independent projection-based classification.

Abstract: Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control

Abstract: Pattern recognition methods for classifying user motion intent based on surface electromyography developed by research groups in well-controlled laboratory conditions are not yet clinically viable for upper limb prosthesis control, due to their limited robustness in users' real-life situations. To address this problem, a novel postprocessing algorithm, aiming to detect and remove misclassifications of a pattern recognition system of forearm and hand motions, is proposed. Using the maximum likelihood calculated by a classifier and the mean global muscle activity of the forearm, an artificial neural network was trained to detect potentially erroneous classification decisions. This system was compared to four previously proposed classification postprocessing methods, in both able-bodied and amputee subjects. Various nonstationarities were included in the experimental protocol to account for challenges posed in real-life settings, such as different contraction levels, static and dynamic motion phases, and effects induced by day-to-day transfers, such as electrode shifts, impedance changes, and psychometric user variability. The improvement in classification accuracy with respect to the unprocessed classifier ranged from 4.8% to 31.6%, depending on the scenarios investigated. The system significantly reduced misclassifications to wrong active classes and is thus a promising approach for improving the robustness of hand prosthesis controllability.

Automatic Ingestion Monitor: A Novel Wearable Device for Monitoring of Ingestive Behavior

Abstract: Objective monitoring of food intake and ingestive behavior in a free-living environment remains an open problem that has significant implications in study and treatment of obesity and eating disorders. In this paper, a novel wearable sensor system (automatic ingestion monitor, AIM) is presented for objective monitoring of ingestive behavior in free living. The proposed device integrates three sensor modalities that wirelessly interface to a smartphone: a jaw motion sensor, a hand gesture sensor, and an accelerometer. A novel sensor fusion and pattern recognition method was developed for subject-independent food intake recognition. The device and the methodology were validated with data collected from 12 subjects wearing AIM during the course of 24 h in which both the daily activities and the food intake of the subjects were not restricted in any way. Results showed that the system was able to detect food intake with an average accuracy of 89.8%, which suggests that AIM can potentially be used as an instrument to monitor ingestive behavior in free-living individuals.

From Passive Tool Holders to Microsurgeons: Safer, Smaller, Smarter Surgical Robots

Abstract: Within only a few decades from its initial introduction, the field of surgical robotics has evolved into a dynamic and rapidly growing research area with increasing clinical uptake worldwide. Initially introduced for stereotaxic neurosurgery, surgical robots are now involved in an increasing number of procedures, demonstrating their practical clinical potential while propelling further advances in surgical innovations. Emerging platforms are also able to perform complex interventions through only a single-entry incision, and navigate through natural anatomical pathways in a tethered or wireless fashion. New devices facilitate superhuman dexterity and enable the performance of surgical steps that are otherwise impossible. They also allow seamless integration of microimaging techniques at the cellular level, significantly expanding the capabilities of surgeons. This paper provides an overview of the significant achievements in surgical robotics and identifies the current trends and future research directions of the field in making surgical robots safer, smaller, and smarter.

An Armband Wearable Device for Overnight and Cuff-Less Blood Pressure Measurement

Abstract: 24-h blood pressure (BP) has significant prognostic value for cardiovascular risk screening, but the present BP devices are mainly cuff-based, which are unsuitable for long-term BP measurement, especially during nighttime. In this paper, we developed an armband wearable pulse transit time (PTT) system for 24-h cuff-less BP measurement and evaluated it in an unattended out-of-laboratory setting. Ten healthy young subjects participated in this ambulatory study, where PTT was measured at 30-min interval by this wearable system and the reference BP was measured by a standard oscillometric ambulatory BP monitor. Due to the misalignment of BP and PTT on their recording time caused by the different measurement principles of the two BP devices, a new estimation method has been proposed: transients in PTT were removed from the raw data by defined criteria, and then evenly interpolated, low-pass filtered, and resampled to synchronize at the time when BP was recorded. The results show that with the proposed method, the correlation between PTT and systolic BP (SBP) during nighttime with dynamic range of 21.8 ± 14.2 mmHg has improved from −0.50 ± 0.24 to −0.62 ± 0.20 $(p , and the difference between the estimated and reference SBP has improved from 0.7 ± 10.7 to 2.8 ± 8.2 mmHg with root mean square error reduced from 10.7 to 8.7 mmHg. In addition, the correlation between a very low frequency component of SBP and PTT obtained from the proposed method during nighttime is −0.80 ± 0.10 and the difference is 2.4 ± 5.7 mmHg for a dynamic BP range of 13.5 ± 8.0 mmHg. It is therefore concluded from this study that the proposed wearable system has great potential to be used for overnight SBP monitoring, especially to measure the averaged SBP over a long period.

A Closed-Loop Brain–Computer Interface Triggering an Active Ankle–Foot Orthosis for Inducing Cortical Neural Plasticity

Abstract: In this paper, we present a brain–computer interface (BCI) driven motorized ankle–foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (∼15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 ± 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motor-evoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for ∼15 min ( $p = 0.009$ and $p , respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size ( $p = 0.38$ and $p = 0.15$ ). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke.

Noncontact monitoring breathing pattern, exhalation flow rate and pulse transit time.

Abstract: We present optical imaging-based methods to measure vital physiological signals, including breathing frequency (BF), exhalation flow rate, heart rate (HR), and pulse transit time (PTT). The breathing pattern tracking was based on the detection of body movement associated with breathing using a differential signal processing approach. A motion-tracking algorithm was implemented to correct random body movements that were unrelated to breathing. The heartbeat pattern was obtained from the color change in selected region of interest (ROI) near the subject's mouth, and the PTT was determined by analyzing pulse patterns at different body parts of the subject. The measured BF, exhaled volume flow rate and HR are consistent with those measured simultaneously with reference technologies (r = 0.98, for HR; r = 0.93, for breathing rate), and the measured PTT difference (30-40 ms between mouth and palm) is comparable to the results obtained with other techniques in the literature. The imaging-based methods are suitable for tracking vital physiological parameters under free-living condition and this is the first demonstration of using noncontact method to obtain PTT difference and exhalation flow rate.

A Submillimetric 3-DOF Force Sensing Instrument With Integrated Fiber Bragg Grating for Retinal Microsurgery

Abstract: Vitreoretinal surgery requires very fine motor control to perform precise manipulation of the delicate tissue in the interior of the eye. Besides physiological hand tremor, fatigue, poor kinesthetic feedback, and patient movement, the absence of force sensing is one of the main technical challenges. Previous two degrees of freedom (DOF) force sensing instruments have demonstrated robust force measuring performance. The main design challenge is to incorporate high sensitivity axial force sensing. This paper reports the development of a submillimetric 3-DOF force sensing pick instrument based on fiber Bragg grating (FBG) sensors. The configuration of the four FBG sensors is arranged to maximize the decoupling between axial and transverse force sensing. A superelastic nitinol flexure is designed to achieve high axial force sensitivity. An automated calibration system was developed for repeatability testing, calibration, and validation. Experimental results demonstrate a FBG sensor repeatability of 1.3 pm. The linear model for calculating the transverse forces provides an accurate global estimate. While the linear model for axial force is only locally accurate within a conical region with a 30° vertex angle, a second-order polynomial model can provide a useful global estimate for axial force. Combining the linear model for transverse forces and nonlinear model for axial force, the 3-DOF force sensing instrument can provide sub-millinewton resolution for axial force and a quarter millinewton for transverse forces. Validation with random samples show the force sensor can provide consistent and accurate measurement of 3-D forces.

Multivariable Adaptive Identification and Control for Artificial Pancreas Systems

Abstract: A constrained weighted recursive least squares method is proposed to provide recursive models with guaranteed stability and better performance than models based on regular identification methods in predicting the variations of blood glucose concentration in patients with Type 1 Diabetes. Use of physiological information from a sports armband improves glucose concentration prediction and enables earlier recognition of the effects of physical activity on glucose concentration. Generalized predictive controllers (GPC) based on these recursive models are developed. The performance of GPC for artificial pancreas systems is illustrated by simulations with UVa-Padova simulator and clinical studies. The controllers developed are good candidates for artificial pancreas systems with no announcements from patients.

Remote Detection of Photoplethysmographic Systolic and Diastolic Peaks Using a Digital Camera

Abstract: We present a new method for measuring photoplethysmogram signals remotely using ambient light and a digital camera that allows for accurate recovery of the waveform morphology (from a distance of 3 m). In particular, we show that the peak-to-peak time between the systolic peak and diastolic peak/inflection can be automatically recovered using the second-order derivative of the remotely measured waveform. We compare measurements from the face with those captured using a contact fingertip sensor and show high agreement in peak and interval timings. Furthermore, we show that results can be significantly improved using orange, green, and cyan color channels compared to the tradition red, green, and blue channel combination. The absolute error in interbeat intervals was 26 ms and the absolute error in mean systolic-diastolic peak-to-peak times was 12 ms. The mean systolic-diastolic peak-to-peak times measured using the contact sensor and the camera were highly correlated, ρ = 0.94 (p <; 0.001). The results were obtained with a camera frame-rate of only 30 Hz. This technology has significant potential for advancing healthcare.