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.

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.

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 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.

Decomposition of magnetoencephalographic data into components corresponding to deep and superficial sources.

We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained by the SSS method can be further decomposed by a simple operation into signals originating from deep and superficial sources within the brain. This is achieved by using a scheme that exploits the beamspace methodology that relies on a linear transformation that maximizes the power of the source space of interest. The efficiency and accuracy of the algorithm are demonstrated by experiments utilizing both simulated and real MEG data.

3-D display and transmission technologies for telemedicine applications: a review.

Three-dimensional (3-D) visualization technologies have been widely commercialized. These technologies have great potential in a number of telemedicine applications, such as teleconsultation, telesurgery, and remote patient monitoring. This work presents an overview of the state-of-the-art 3-D display devices and related 3-D image/video transmission technologies with the goal of enhancing their utilization in medical applications.

Intraoperative somatosensory evoked potential monitoring during anterior cervical discectomy and fusion in nonmyelopathic patients--a review of 1,039 cases.

BACKGROUND CONTEXT: Intraoperative somatosensory evoked potential (SSEP) monitoring has been shown to reduce the incidence of new postoperative neurological deficits in scoliosis surgery. However, its usefulness during cervical spine surgery remains a subject of debate. PURPOSE: To determine the utility of intraoperative SSEP monitoring in a specific patient population (those with cervical radiculopathy in the absence of myelopathy) who underwent anterior cervical discectomy and fusion (ACDF) surgery. STUDY DESIGN: Retrospective review. PATIENT SAMPLE: A total of 1,039 nonmyelopathic patients who underwent single or multilevel ACDF surgery. The control group (462 patients) did not have intraoperative SSEP monitoring, whereas the monitored group (577 patients) had continuous intraoperative SSEP monitoring performed. OUTCOME MEASURE: A new postoperative neurological deficit. METHODS: SSEP tracings were reviewed for all 577 patients in the monitored group and all significant signal changes were noted. Medical records were reviewed for all 1,039 patients to determine if any new neurological deficits developed in the immediate postoperative period. RESULTS: None of the patients in the control group had any new postoperative neurological deficits. In the monitored group there were six instances of transient SSEP changes (1 due to suspected carotid artery compression; 5 thought to be due to transient hypotension) which resolved with the appropriate intraoperative intervention (repositioning of retractors; raising the arterial blood pressure). Upon waking up from anesthesia, one patient in the monitored group had a new neurological deficit (partial central cord syndrome) despite normal intraoperative SSEP signals. CONCLUSIONS: ACDF appears to be a safe surgical procedure with a low incidence of iatrogenic neurological injury. Transient SSEP signal changes, which improved with intraoperative interventions, were not associated with new postoperative neurological deficits. An intraoperative neurological deficit is possible despite normal SSEP signals.

Computer simulation of corticospinal activity during Transcranial Electrical Stimulation in neurosurgery.

Transcranial Electrical Stimulation (TES) is an important procedure in intraoperative motor monitoring. When neurosurgery is performed at certain difficult locations within the central nervous system (CNS), TES evaluates CNS functions during surgical manipulations to prevent post-operative complications. In TES, electrical stimulation is provided to the motor cortex through electrodes placed on the scalp, generating action potentials which travel through the nervous system. Despite widespread use, the sites of activation (AP generation) within the brain are not well understood. We have integrated computational and neurophysiologic models including a 3D volume conduction head model computed using the finite element method, a realistic corticospinal tract (CST) model, and a geometry-specific axon activation model for the CST to predict the sites of activation along the CST as a function of electrode placement and stimulation voltage, which have been verified by epidural recordings. We then develop a simple meshing and rendering algorithm to display the activating function along the CST. We have found that the AP generation appears closely linked to regions of high CST curvature. Our model and rendering algorithm provide a window to visualize the effects of TES in the brain.

An overview of 3D video transmission and display technologies for telemedicine applications.

Digital 3D visualization provides a fundamental platform to render both the real world and computer generated objects in a highly comprehensive form. It has a number of significant applications in telemedicine, such as telesurgery, patient monitoring and remote surgeon training. Towards utilizing the 3D visualization technologies for these applications, we present an overview of state-of-the-art 3D display devices and discuss related data transmission technologies to support the remote 3D display.

Design of the next-generation medical implants with communication energy and ports.

In this work, we provide an effective solution to the communication and power supply problems in miniature medical devices implanted within the human body. The volume conduction property of the human tissue is utilized as a natural cable for the delivery of both information and energy. A practical design is presented consisting of a small, simple, and convenient external device called an energy pad.

Platform technologies to support brain-computer interfaces.

There is a lack of adequate and cost-effective treatment options for many neurodegenerative diseases. The number of affected patients is in the millions, and this number will only increase as the population ages. The developing areas of neuromimetics and stimulative implants provide hope for treatment, as evidenced by the currently available, but limited, implants. New technologies are emerging that are leading to the development of highly intelligent, implantable sensors, activators, and mobile robots that will provide in vivo diagnosis, therapeutic interventions, and functional replacement. Two key platform technologies that are required to facilitate the development of these neuromimetic and stimulative implants are data communication channels and the devices' power supplies. In the research reported in this paper, investigators have examined the use of novel concepts that address these two needs. These concepts are based on ionic volume conduction (VC) to provide a natural communication channel to support the functioning of these devices, and on biofuel cells to provide a continuously rechargeable power supply that obtains electrons from the natural metabolic pathways. The fundamental principles of the VC communication channels, including novel antenna design, are demonstrated. These principles include the basic mechanisms, device sensitivity, bidirectionality of communication, and signal recovery. The demonstrations are conducted using mathematical and finite element analysis, physical experiments, and animal experiments. The fundamental concepts of the biofuel cells are presented, and three versions of the cells that have been studied are discussed, including bacteria-based cells and two white cell-based experiments. In this paper the authors summarize the proof or principal experiments for both a biomimetic data channel communication method and a biofuel cell approach, which promise to provide innovative platform technologies to support complex devices that will be ready for implantation in the human nervous system in the next decade.

Direct and mediated associations between P300 amplitude in childhood and substance use disorders outcome in young adulthood.

BACKGROUND: Attenuated amplitude of the P300 wave has been frequently documented in youths at high risk for substance use disorders (SUDs). This investigation had two aims: 1) determine whether P300 amplitude in late childhood is a predictor of SUD outcome by age 19; and 2) evaluate whether neurobehavior disinhibition (ND) mediates this association. METHODS: Boys (aged 10-12) were recruited through proband biological fathers with either a lifetime DSM-III-R diagnosis of SUD (N = 67) or no adult psychiatric disorder (N = 94). P300 amplitude was recorded during an auditory oddball task. Neurobehavior disinhibition was evaluated using tests of executive cognitive function, behavior under control, and emotion dysregulation. The relationship is assessed using multiple and logistic regressions. RESULTS: Substance use disorder by age 19 was significantly predicted by P300 amplitude (odds ratio [OR] = .958; 95% confidence interval [CI] = .918-.999; Wald chi (2) = 3.992; df = 1; p = .045) and ND score (OR = 1.060; 95% CI = 1.023-1.098; Wald chi (2) = 10.267; df = 1; p = .0014) measured at age 10 to 12. P300 amplitude also significantly correlated with ND severity (beta = -.217; t = -2.412; df = 6,160; p = .017). Low P300 amplitudes were observed in children who succumbed to SUD by age 19. CONCLUSIONS: These results indicate that ND mediates the association between attenuated P300 amplitude in childhood and SUD at age 19; however, P300 amplitude is not a specific childhood marker of SUD.

Association among parental substance use disorder, p300 amplitude, and neurobehavioral disinhibition in preteen boys at high risk for substance use disorder.

The P300 amplitude of the event-related potential as a mediator of the association between parental substance use disorder (SUD) and child's neurobehavioral disinhibition was assessed. The P300 amplitude was recorded using an oddball task in sons of fathers having either lifetime SUD (n = 105) or no psychiatric disorder (n = 160). Neurobehavioral disinhibition was assessed using measures of affect regulation, behavior control, and executive cognitive function. Parental SUD and child's P300 amplitude accounted for, respectively, 16.6% and 16.8% of neurobehavioral disinhibition variance. Controlling for parental and child psychopathology, an association between parental SUD and child's P300 amplitude was not observed. It was concluded that the P300 amplitude does not mediate the association between parental SUD and child's neurobehavioral disinhibition.

A neuro-fuzzy inference system for modeling and prediction of heart rate variability in the neuro-intensive care unit.

In the neurological intensive care unit (NICU), prediction of impending changes in patient condition would be highly beneficial. In this paper, we employ a neuro-fuzzy inference system (NFIS) for short-term prediction of heart rate variability in the NICU. An NFIS was selected because it allows for a "gray-box" approach through which a system identification procedure is used in conjunction with fuzzy modeling. The NFIS is described in detail and is compared to an auto-regressive moving average (ARMA) model for its ability to model both simulated and measured data from NICU patients. We found that the NFIS is capable of predicting changes in heart rate to a reasonable extent, and that the NFIS has both advantages and limitations over the ARMA model. The NFIS may therefore be a reasonable technique to consider for more extensive prediction purposes in ICU settings.

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.

Data communication between brain implants and computer.

Recent advances in neuroscience, microelectronics, and information technology have allowed construction of miniature, but highly intelligent, devices to be implanted within the brain to perform in vitro diagnostic and therapeutic functions. However, there exists a significant problem in establishing an effective wireless data communication link between brain implants and external computer. This communication investigates this link and presents a new design using the mechanism of volume conduction of biological tissues. A theoretical model of volume conduction of the head is utilized to compute signal strength in data communication and the result is evaluated by a physical model. The two-way data communication sensitivity of the volume conduction channel is found to be symmetric, as suggested by the reciprocity theorem. A high-performance, x-shaped volume conduction antenna has been designed. Experiments are performed on animals which demonstrate the effectiveness of this volume conduction approach.

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).

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.

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.

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.