Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation.

Advancements in electrode technologies to both stimulate and record the central nervous system's electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.

Neurologic Complications in Monitored versus Unmonitored Image-Guidance Assisted Posterior Lumbar Instrumentation.

BACKGROUND: Intraoperative neurophysiologic monitoring (IOM) has been used clinically since the 1970s and is a reliable tool for detecting impending neurologic compromise. However, there are mixed data as to whether long-term neurologic outcomes are improved with its use. We investigated whether IOM used in conjunction with image guidance produces different patient outcomes than with image guidance alone. METHODS: We reviewed 163 consecutive cases between January 2015 and December 2018 and compared patients undergoing posterior lumbar instrumentation with image guidance using and not using multimodal IOM. Monitored and unmonitored surgeries were performed by the same surgeons, ruling out variability in intersurgeon technique. Surgical and neurologic complication rates were compared between these 2 cohorts. RESULTS: A total of 163 patients were selected (110 in the nonmonitored cohort vs. 53 in the IOM cohort). Nineteen signal changes were noted. Only 3 of the 19 patients with signal changes had associated neurologic deficits postoperatively (positive predictive value 15.7%). There were 5 neurologic deficits that were observed in the nonmonitored cohort and 8 deficits observed in the monitored cohort. Transient neurologic deficit was significantly higher in the monitored cohort per case (P < 0.0198) and per screw (P < 0.0238); however, there was no difference observed between the 2 cohorts when considering permanent neurologic morbidity per case (P < 0.441) and per screw (P < 0.459). CONCLUSIONS: The addition of IOM to cases using image guidance does not appear to decrease long-term postoperative neurologic morbidity and may have a reduced diagnostic role given availability of intraoperative image-guidance systems.

A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG.

OBJECTIVE: Intraoperative neurophysiological monitoring (IONM) is the use of electrophysiological methods during certain high-risk surgeries to assess the functional integrity of nerves in real time and alert the surgeon to prevent damage. However, the efficiency of IONM in current practice is limited by latency of verbal communications, inter-rater variability, and the subjective manner in which electrophysiological signals are described. METHODS: In an attempt to address these shortcomings, we investigate automated classification of free-running electromyogram (EMG) waveforms during IONM. We propose a hybrid model with a convolutional neural network (CNN) component and a long short-term memory (LSTM) component to better capture complicated EMG patterns under conditions of both electrical noise and movement artifacts. Moreover, a preprocessing pipeline based on data normalization is used to handle classification of data from multiple subjects. To investigate model robustness, we also analyze models under different methods for processing of artifacts. RESULTS: Compared with several benchmark modeling methods, CNN-LSTM performs best in classification, achieving accuracy of 89.54% and sensitivity of 94.23% in cross-patient evaluation. CONCLUSION: The CNN-LSTM model shows promise for automated classification of continuous EMG in IONM. SIGNIFICANCE: This technique has potential to improve surgical safety by reducing cognitive load and inter-rater variability.

An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle.

Recently, wearable computers have become new members in the family of mobile electronic devices, adding new functions to those provided by smart-phones and tablets. As "always-on" miniature computers in the personal space, they will play increasing roles in the field of healthcare. In this work, we present our development of eButton, a wearable computer designed as a personalized, attractive, and convenient chest pin in a circular shape. It contains a powerful microprocessor, numerous electronic sensors, and wireless communication links. We describe its design concepts, electronic hardware, data processing algorithms, and its applications to the evaluation of diet, physical activity and lifestyle in the study of obesity and other chronic diseases.

Cuff-Free Blood Pressure Estimation Using Pulse Transit Time and Heart Rate.

It has been reported that the pulse transit time (PTT), the interval between the peak of the R-wave in electrocardiogram (ECG) and the fingertip photoplethysmogram (PPG), is related to arterial stiffness, and can be used to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP). This phenomenon has been used as the basis to design portable systems for continuously cuff-less blood pressure measurement, benefiting numerous people with heart conditions. However, the PTT-based blood pressure estimation may not be sufficiently accurate because the regulation of blood pressure within the human body is a complex, multivariate physiological process. Considering the negative feedback mechanism in the blood pressure control, we introduce the heart rate (HR) and the blood pressure estimate in the previous step to obtain the current estimate. We validate this method using a clinical database. Our results show that the PTT, HR and previous estimate reduce the estimated error significantly when compared to the conventional PTT estimation approach (p<0.05).

Early major neurologic complications after lung transplantation: incidence, risk factors, and outcome.

BACKGROUND: Early major neurologic complications after lung transplantation represent a major source of morbidity for patients and compromise their quality of life; however, the mechanisms underlying neurologic complications and their impact on outcomes in lung transplantation remain largely unknown. METHODS: Patients who received lung transplants at our institution between January 2004 and December 2010 were identified (n=759). Data on complications including occurrence, timing, management, and outcome were extracted from our transplant database and medical record review. Major neurologic complications were defined as those that were potentially life threatening, required urgent treatment/intubation, or required admission to the intensive care unit. RESULTS: Seventy (9.2%) patients experienced major neurologic complications within 2 weeks after lung transplantation. Most common complications were stroke (41%) and severe toxic/metabolic encephalopathy (37%). Multivariate analysis demonstrated that advanced age, history of coronary artery disease, prolonged use of cardiopulmonary bypass, and severe primary graft dysfunction increased the risk for death in patients with early major neurologic complications (P<0.05). There was a significant difference in survival between patients with neurologic complications and without (90-day mortality: 15% of patients who developed neurologic complications versus 4% of patients who did not; P=0.03; 5-year survival: 51.1% in patients who developed neurologic complication versus 62.1% in patients who did not; P<0.05). CONCLUSIONS: Early major neurologic complications after lung transplantation are common and carry substantial morbidity and mortality. Given the risk factors identified in this study, additional pretransplantation workup and intraoperative and postoperative monitoring for high-risk patients may help reduce the incidence of neurologic complications.

Design of a Wireless EEG System for Point-of-Care Applications.

This study aims to develop a wireless EEG system to provide critical point-of-care information about brain electrical activity. A novel dry electrode, which can be installed rapidly, is used to acquire EEG from the scalp. A wireless data link between the electrode and a data port (i.e., a smartphone) is established based on the Bluetooth technology. A prototype of this system has been implemented and its performance in acquiring EEG has been evaluated.

Imaged based estimation of food volume using circular referents in dietary assessment.

Measuring food volume (portion size) is a critical component in both clinical and research dietary studies. With the wide availability of cell phones and other camera-ready mobile devices, food pictures can be taken, stored or transmitted easily to form an image based dietary record. Although this record enables a more accurate dietary recall, a digital image of food usually cannot be used to estimate portion size directly due to the lack of information about the scale and orientation of the food within the image. The objective of this study is to investigate two novel approaches to provide the missing information, enabling food volume estimation from a single image. Both approaches are based on an elliptical reference pattern, such as the image of a circular pattern (e.g., circular plate) or a projected elliptical spotlight. Using this reference pattern and image processing techniques, the location and orientation of food objects and their volumes are calculated. Experiments were performed to validate our methods using a variety of objects, including regularly shaped objects and food samples.

A low-impedance, skin-grabbing, and gel-free EEG electrode.

Inspired by the extraordinary object grabbing ability of certain insects (e.g., a grasshopper), we have developed a novel dry EEG electrode, called the skin screw electrode. Unlike the traditional disc electrode which requires several minutes to install, the installation of the skin screw electrode can be completed within seconds since no skin preparation and electrolyte application are required. Despite the drastic improvement in the installation time, our experiments have demonstrated that the skin screw electrode has a similar impedance value to that of the disc electrode. The skin screw electrode has a wide range of applications, such as clinical EEG diagnosis, epilepsy monitoring, emergency medicine, and home-based human-computer interface.

Physical Activity Recognition Based on Motion in Images Acquired by a Wearable Camera.

A new technique to extract and evaluate physical activity patterns from image sequences captured by a wearable camera is presented in this paper. Unlike standard activity recognition schemes, the video data captured by our device do not include the wearer him/herself. The physical activity of the wearer, such as walking or exercising, is analyzed indirectly through the camera motion extracted from the acquired video frames. Two key tasks, pixel correspondence identification and motion feature extraction, are studied to recognize activity patterns. We utilize a multiscale approach to identify pixel correspondences. When compared with the existing methods such as the Good Features detector and the Speed-up Robust Feature (SURF) detector, our technique is more accurate and computationally efficient. Once the pixel correspondences are determined which define representative motion vectors, we build a set of activity pattern features based on motion statistics in each frame. Finally, the physical activity of the person wearing a camera is determined according to the global motion distribution in the video. Our algorithms are tested using different machine learning techniques such as the K-Nearest Neighbor (KNN), Naive Bayesian and Support Vector Machine (SVM). The results show that many types of physical activities can be recognized from field acquired real-world video. Our results also indicate that, with a design of specific motion features in the input vectors, different classifiers can be used successfully with similar performances.

A metabolic biofuel cell: conversion of human leukocyte metabolic activity to electrical currents.

An investigation of the electrochemical activity of human white blood cells (WBC) for biofuel cell (BFC) applications is described. WBCs isolated from whole human blood were suspended in PBS and introduced into the anode compartment of a proton exchange membrane (PEM) fuel cell. The cathode compartment contained a 50 mM potassium ferricyanide solution. Average current densities between 0.9 and 1.6 µA cm-2 and open circuit potentials (Voc) between 83 and 102 mV were obtained, which were both higher than control values. Cyclic voltammetry was used to investigate the electrochemical activity of the activated WBCs in an attempt to elucidate the mechanism of electron transfer between the cells and electrode. Voltammograms were obtained for the WBCs, including peripheral blood mononuclear cells (PBMCs - a lymphocyte-monocyte mixture isolated on a Ficoll gradient), a B lymphoblastoid cell line (BLCL), and two leukemia cell lines, namely K562 and Jurkat. An oxidation peak at about 363 mV vs. SCE for the PMA (phorbol ester) activated primary cells, with a notable absence of a reduction peak was observed. Oxidation peaks were not observed for the BLCL, K562 or Jurkat cell lines. HPLC confirmed the release of serotonin (5-HT) from the PMA activated primary cells. It is believed that serotonin, among other biochemical species released by the activated cells, contributes to the observed BFC currents.

Recognizing physical activity from ego-motion of a camera.

A new image based activity recognition method for a person wearing a video camera below the neck is presented in this paper. The wearable device is used to capture video data in front of the wearer. Although the wearer never appears in the video, his or her physical activity is analyzed and recognized using the recorded scene changes resulting from the motion of the wearer. Correspondence features are extracted from adjacent frames and inaccurate matches are removed based on a set of constraints imposed by the camera model. Motion histograms are defined and calculated within a frame and we define a new feature called accumulated motion distribution derived from motion statistics in each frame. A Support Vector Machine (SVM) classifier is trained with this feature and used to classify physical activities in different scenes. Our results show that different types of activities can be recognized in low resolution, field acquired real-world video.

Automatic detection of dining plates for image-based dietary evaluation.

An automatic detector that finds circular dining plates in chronically recorded images or videos is reported for the study of food intake and obesity. We first detect edges from input images. After a number of processing steps that convert edges into curves, arc filtering and grouping algorithms are applied. Then, convex hulls are identified and the ones that fit the description of ellipses corresponding to dining plates are determined. Our experiments using real-world images indicate that this detector is highly reliable and robust even when the input images contain complex background scenes and the dining plates are severely occluded.

Novel hydrogel-based preparation-free EEG electrode.

The largest obstacles to signal transduction for electroencephalography (EEG) recording are the hair and the epidermal stratum corneum of the skin. In typical clinical situations, hair is parted or removed, and the stratum corneum is either abraded or punctured using invasive penetration devices. These steps increase preparation time, discomfort, and the risk of infection. Cross-linked sodium polyacrylate gel swelled with electrolyte was explored as a possible skin contact element for a prototype preparation-free EEG electrode. As a superabsorbent hydrogel, polyacrylate can swell with electrolyte solution to a degree far beyond typical contemporary electrode materials, delivering a strong hydrating effect to the skin surface. This hydrating power allows the material to increase the effective skin contact surface area through wetting, and noninvasively decrease or bypass the highly resistive barrier of the stratum corneum, allowing for reduced impedance and improved electrode performance. For the purposes of the tests performed in this study, the polyacrylate was prepared both as a solid elastic gel and as a flowable paste designed to penetrate dense scalp hair. The gel can hold 99.2% DI water or 91% electrolyte solution, and the water content remains high after 29 h of air exposure. The electrical impedance of the gel electrode on unprepared human forearm is significantly lower than a number of commercial ECG and EEG electrodes. This low impedance was maintained for at least 8 h (the longest time period measured). When a paste form of the electrode was applied directly onto scalp hair, the impedance was found to be lower than that measured with commercially available EEG paste applied in the same manner. Time-frequency transformation analysis of frontal lobe EEG recordings indicated comparable frequency response between the polyacrylate-based electrode on unprepared skin and the commercial EEG electrode on abraded skin. Evoked potential recordings demonstrated signal-to-noise ratios of the experimental and commercial electrodes to be effectively equivalent. These results suggest that the polyacrylate-based electrode offers a powerful option for EEG recording without scalp preparation.

Temporal postural synergies of the hand in rapid grasping tasks.

Postural synergies of the hand have been widely proposed in the literature, but only a few attempts were made to visualize temporal postural synergies, i.e., profiles of postural synergies varying over time. This paper aims to derive temporal postural synergies from kinematic synergies extracted from joint angular velocity profiles of rapid grasping movements. The rapid movements constrain the kinematic synergies to combine instantaneously, and thus, the movements can be approximated by a weighted summation of synchronous synergies. After being extracted by using singular value decomposition, the synchronous kinematic synergies were translated into temporal postural synergies, which revealed strategies of enslaving, metacarpal flexion for larger movements, and hierarchical recruitment of joints, adapted by subjects while grasping.

Dimensionality reduction in control and coordination of the human hand.

The concept of kinematic synergies is proposed to address the dimensionality reduction problem in control and coordination of the human hand. This paper develops a method for extracting kinematic synergies from joint-angular-velocity profiles of hand movements. Decomposition of a limited set of synergies from numerous movements is a complex optimization problem. This paper splits the decomposition process into two stages. The first stage is to extract synergies from rapid movement tasks using singular value decomposition (SVD). A bank of template functions is then created from shifted versions of the extracted synergies. The second stage is to find weights and onset times of the synergies based on l(1) -minimization, whose solutions provide sparse representations of hand movements using synergies.

Wireless energy transfer platform for medical sensors and implantable devices.

Witricity is a newly developed technique for wireless energy transfer. This paper presents a frequency adjustable witricity system to power medical sensors and implantable devices. New witricity resonators are designed for both energy transmission and reception. A prototype platform is described, including an RF power source, two resonators with new structures, and inductively coupled input and output stages. In vitro experiments, both in open air and using a human head phantom consisting of simulated tissues, are employed to verify the feasibility of this platform. An animal model is utilized to evaluate in vivo energy transfer within the body of a laboratory pig. Our experiments indicate that witricity is an effective new tool for providing a variety of medical sensors and devices with power.

Time encoding and reconstruction of multichannel data by brain implants using asynchronous sigma delta modulators.

Recently, information technology and microelectronics have enabled implanting miniature and highly intelligent devices within the brain for in-vitro diagnostic and therapeutic functions. Power and physical size constraints of these devices necessitate novel signal processing methods. In this paper we investigate an effective data acquisition and reconstruction method for brain implants based on Asynchronous Sigma Delta Modulators (ASDMs). The ASDMs are analog non-linear feedback systems capable of time coding signals. The proposed reconstruction algorithm is based on the Prolate Spheroidal Wave Function (PSWF) expansion of the sinc functions and the order of expansion is given by the input signal being coded. Multiplexing and transmission of the different channels of data are accomplished by chirp orthogonal frequency division multiplexing. Computer simulations using multi channel electroencephalographic data are performed for wireless transmission by brain implants for monitoring abnormal brain activities of epilepsy patients.