INTRODUCTION:Atrial Fibrillation (AF) is the most common cardiac arrhythmia, presenting a significant independent risk factor for stroke and thromboembolism. With the emergence of m-Health devices, the importance of automatic detection of AF in an off-clinic setting is growing. This study demonstrates the performance of a bimodal classifier for distinguishing AF from sinus rhythm (SR) that could be used for automated detection of AF episodes.METHODS:Surface recordings from a hand-held research device and standard electrocardiograms (ECG) were collected and analyzed from 68 subjects. An additional 48 subjects from the MIT-BIH Arrythmia Database were also analyzed. All ECGs were blindly reviewed by physicians independently of the bimodal algorithm analysis. The algorithm selects an artifact-free 6-s ECG segment out of a 20-s long recording and computes a spectral Frequency Dispersion Metric (FDM) and a temporal R-R interval variability (VRR) index.RESULTS:Scatter plots of the VRR and FDM indices revealed two distinct clusters. The bimodal scattering of the indices revealed a linear classification boundary that could be employed to differentiate the SR from AF waveforms. The selected classification boundary was able to correctly differentiate all the subjects from both datasets into either SR or AF groups, except for 3 SR subjects from the MIT-BIH dataset.CONCLUSION:Our bimodal classification algorithm was demonstrated to successfully acquire, analyze and interpret ECGs for the presence of AF indicating its potential to support m-Health diagnosis, monitoring, and management of therapy in AF patients.

The Department of Defense, Department of Veterans Affairs and National Institutes of Health have invested significantly in advancing prosthetic technologies over the past 25 years, with the overall intent to improve the function, participation and quality of life of Service Members, Veterans, and all United States Citizens living with limb loss. These investments have contributed to substantial advancements in the control and sensory perception of prosthetic devices over the past decade. While control of motorized prosthetic devices through the use of electromyography has been widely available since the 1980s, this technology is not intuitive. Additionally, these systems do not provide stimulation for sensory perception. Recent research has made significant advancement not only in the intuitive use of electromyography for control but also in the ability to provide relevant meaningful perceptions through various stimulation approaches. While much of this previous work has traditionally focused on those with upper extremity amputation, new developments include advanced bidirectional neuroprostheses that are applicable to both the upper and lower limb amputation. The goal of this review is to examine the state-of-the-science in the areas of intuitive control and sensation of prosthetic devices and to discuss areas of exploration for the future. Current research and development efforts in external systems, implanted systems, surgical approaches, and regenerative approaches will be explored.

The BRAIN Initiative arose from a grand challenge to "accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought." The BRAIN Initiative is a public-private effort focused on the development and use of powerful tools for acquiring fundamental insights about how information processing occurs in the central nervous system (CNS). As the Initiative enters its fifth year, NIH has supported >500 principal investigators, who have answered the Initiative's challenge via hundreds of publications describing novel tools, methods, and discoveries that address the Initiative's seven scientific priorities. We describe scientific advances produced by individual laboratories, multi-investigator teams, and entire consortia that, over the coming decades, will produce more comprehensive and dynamic maps of the brain, deepen our understanding of how circuit activity can produce a rich tapestry of behaviors, and lay the foundation for understanding how its circuitry is disrupted in brain disorders. Much more work remains to bring this vision to fruition, and the National Institutes of Health continues to look to the diverse scientific community, from mathematics, to physics, chemistry, engineering, neuroethics, and neuroscience, to ensure that the greatest scientific benefit arises from this unique research Initiative.

OBJECTIVES:To perform a pilot study using transcutaneous electrical nerve stimulation (TENS) on the dorsal genital nerve and the posterior tibial nerve for improving symptoms of female sexual dysfunction (FSD) in women without bladder problems. We hypothesize that this therapy will be effective at improving genital arousal deficits.MATERIALS AND METHODS:Nine women with general FSD completed the study. Subjects received 12 sessions of transcutaneous dorsal genital nerve stimulation (DGNS; n = 6) or posterior tibial nerve stimulation (PTNS; n = 3). Stimulation was delivered for 30 min at 20 Hz. Sexual functioning was evaluated with the female sexual functioning index (FSFI), and surveys were also given on general health, urological functioning, and the Patients' Global Impression of Change (PGIC) after treatment. Surveys were given before treatment (baseline), after 6 and 12 weeks of treatment, and 6 weeks after the completion of stimulation sessions.RESULTS:The average total FSFI score across all subjects significantly increased from 15.3 ± 4.8 at baseline to 20.3 ± 7.8 after six sessions, 21.7 ± 7.5 after 12 sessions, and 21.3 ± 7.1 at study completion (p < 0.05 for all time points). Increases were observed in both DGNS and PTNS subjects. Significant FSFI increases were seen in the subdomains of lubrication, arousal, and orgasm, each of which is related to genital arousal. Bladder and general health surveys did not change across the study. PGIC had a significant increase.CONCLUSIONS:This study provides evidence that transcutaneous stimulation of peripheral nerves has the potential to be a valuable therapeutic tool for women with FSD.

Nick B. Langhals, Ph.D. serves as the Program Director for Neural Engineering at the National Institute of Neurological Disorders and Stroke (NINDS), where he manages a portfolio of grants focused on the development and translations of neurotechnologies. He is heavily involved in the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative teams focused on both implantable and non-invasive technologies for recording and modulating neural activity. He also serves as the NINDS lead for the Bioengineering Research and Stimulating Peripheral Activity to Relieve Conditions (SPARC) programs at NIH. Within this talk, he will discuss many of the recent advances in neural interface materials, devices, electronics, and instrumentation that have been developed through NIH BRAIN funding in many of these areas. Further, he will also discuss a vision for future opportunities in research and development in these areas and where the electrochemical community could play an active role.

AMERICAN SOCIETY OF PLASTIC SURGEONS PLASTIC & RECONSTRUCTIVE SURGERY PRS GLOBAL OPEN ASPS EDUCATION NETWORK AMERICAN SOCIETY OF PLASTIC SURGEONS PLASTIC & RECONSTRUCTIVE SURGERY PRS GLOBAL OPEN ASPS EDUCATION NETWORK

AMERICAN SOCIETY OF PLASTIC SURGEONS PLASTIC & RECONSTRUCTIVE SURGERY PRS GLOBAL OPEN ASPS EDUCATION NETWORK AMERICAN SOCIETY OF PLASTIC SURGEONS PLASTIC & RECONSTRUCTIVE SURGERY PRS GLOBAL OPEN ASPS EDUCATION NETWORK

A syringe-implantable electrocardiography (ECG) monitoring system is proposed. The noise optimization and circuit techniques in the analog front-end (AFE) enable 31 nA current consumption while a minimum energy computation approach in the digital back-end reduces digital energy consumption by 40%. The proposed SoC is fabricated in 65 nm CMOS and consumes 64 nW while successfully detecting atrial fibrillation arrhythmia and storing the irregular waveform in memory in experiments using an ECG simulator, a live sheep, and an isolated sheep heart.

Background High-fidelity volitional control of bioengineered prosthetic limbs with multiple degrees of freedom requires the implantation of multiple recording interfaces to detect independent control signals. However, interface utilization is complicated by interfering electrophysiological signals originating from surrounding muscles and nerves, leading to equivocal signal detection. We developed and validated a surgical model to characterize signal propagation through various biomaterials to identify insulating substrates for use in implantable interfaces. The identification of these insulating materials will facilitate the acquisition of noncontaminated prosthetic control signals, thus improving manipulation of advanced prosthetic limbs.Methods Using a rat hindlimb model, 4 groups (n = 8/group) were tested. A medial gastrocnemius muscle flap was elevated, leaving the neurovascular pedicle intact. The flap was rotated into a chamber and secured to a silicone base. A stainless steel electrode was affixed to the surface of a muscle and encircled by 1-layer small intestinal submucosa (SIS), 4-layer SIS, silicone elastomer, or nothing (uninsulated). A superimposing electrode was attached, and an external silicone layer was wrapped around the construct and sutured in place. Electromyographic studies were then performed.Results This model was found to correspond with expected signal isolation characteristics of the nonconductive silicone group, electrically inert single and multilayer SIS group, and the uninsulated group. Signal isolation of compound muscle action potential amplitude at stimulation threshold was significantly greater using silicone (51.4%) compared with the 1-layer SIS (-6.8%), 4-layer SIS (-3.3% ), or uninsulated groups (1.2%) (P = <0.001). Isolation of the maximum compound muscle action potential peak-to-peak amplitude was also greater with silicone (56.7%) versus the 1-layer SIS (1.5%), 4-layer SIS (1.1%), or uninsulated groups (-0.7%) (P = <0.001).Conclusions This study demonstrates and validates a novel surgical model to characterize in vivo signal propagation and subsequently identify insulating materials for use in implantable interface systems currently in development. Improved signal isolation through the utilization of these materials stands to greatly improve control fidelity of neuroprosthetic limbs.

AMERICAN SOCIETY OF PLASTIC SURGEONS PLASTIC & RECONSTRUCTIVE SURGERY PRS GLOBAL OPEN ASPS EDUCATION NETWORK AMERICAN SOCIETY OF PLASTIC SURGEONS PLASTIC & RECONSTRUCTIVE SURGERY PRS GLOBAL OPEN ASPS EDUCATION NETWORK

Introduction: Home telemetry monitoring with accurate automated rhythm classification can have important clinical benefits in the timely diagnosis and appropriate management of patients with atrial fibrillation (AF). We clinically validated a novel personal e-Health device and algorithm developed to distinguish AF from sinus rhythm (SR). Methods: A handheld electrocardiogram (ECG) recording system (Maestro) and signal processing platform were developed. The Maestro provides an LCD interface that continuously shows an ECG, heart rate, and heart rhythm status. Twenty second ECG signals analogous to Lead I were acquired from 66 patients presenting to the arrhythmia clinic at the University of Michigan Hospital either in SR or AF. Electrograms were segmented into non-overlapping 6-second samples and one random segment per patient was selected for analysis by the Maestro system. Simultaneous 5 or 12 lead ECGs were obtained from these patients and 3 expert physicians blinded to the Maestro analysis identified the rhythm as SR or AF. The Maestro system applied several signal conditioning algorithms to each ECG sample. The dimensionless temporal R-R interval variability (VRR) index and spectral frequency dispersion metric (FDM) were computed. Results: The 2-dimensional scatter-gram of the samples demonstrated 2 distinct clusters of VRR and FDM for patients with SR and AF. The VRR index clusters for SR and AF patients were 0.018 ± 0.013 and 0.187 ± 0.073 (mean±std), respectively (p < 0.001). The FDM clusters for SR and AF patients occurred at 10.5 ± 5.916 and 15.892 ± 3.337, respectively (p < 0.001). We developed a Gaussian Mixed Model (GMM) classifier to distinguish between the AF and SR clusters. Only after the GMM classifier was obtained were the Maestro classifications compared to the physicians’ readings. The algorithm correctly categorized AF (N = 46) and SR (N = 20) for all Maestro segments analyzed with 100% specificity and sensitivity. Conclusion: The Maestro handheld telemetry unit utilizes a novel classification algorithm and was demonstrated to acquire and automatically analyze 6-second electrograms for rapid and accurate classification of patients in SR or AF in this initial clinical validation trial.

Each year, approximately 185,000 Americans suffer the devastating loss of a limb. The effects of upper limb amputations are profound because a person's hands are tools for everyday functioning, expressive communication, and other uniquely human attributes. Despite the advancements in prosthetic technology, current upper limb prostheses are still limited in terms of complex motor control and sensory feedback. Sensory feedback is critical to restoring full functionality to amputated patients because it would relieve the cognitive burden of relying solely on visual input to monitor motor commands and provide tremendous psychological benefits. This article reviews the latest innovations in sensory feedback and argues in favor of peripheral nerve interfaces. First, the authors examine the structure of the peripheral nerve and its importance in the development of a sensory interface. Second, the authors discuss advancements in targeted muscle reinnervation and direct neural stimulation by means of intraneural electrodes. The authors then explore the future of prosthetic sensory feedback using innovative technologies for neural signaling, specifically, the sensory regenerative peripheral nerve interface and optogenetics. These breakthroughs pave the way for the development of a prosthetic limb with the ability to feel.

BACKGROUND:This study compared epimysial patch electrodes with intramuscular hook electrodes using monopolar and bipolar recording configurations. The purpose was to determine which strategy transduced muscle signals with better fidelity for control of myoelectric prostheses.METHODS:One of the two electrode styles, patch (n = 4) or hook (n = 6) was applied to the left extensor digitorum longus muscle in rats. Electrodes were evaluated at the time of placement and at monthly intervals for 4 months. Evaluations consisted of evoked electromyography signals from stimulation pulses applied to the peroneal and tibial nerves in both monopolar and bipolar recording configurations.RESULTS:Compared with hook electrodes, patch electrodes recorded larger signals of interest and minimized muscle tissue injury. A bipolar electrode configuration significantly reduced signal noise when compared with a monopolar configuration.CONCLUSION:Epimysial patch electrodes outperform intramuscular hook electrodes during chronic skeletal muscle implantation.