Swallowing accelerometry has been recently investigated as a potential non-invasive tool for dysphagia screening. This method is based on the translation of vibrations recorded from the upper aerodigestive tract structure during swallowing into a voltage signal. Some studies hypothesize the hyoid bone movement during swallowing as the source of swallow vibrations, as it is an essential component of swallowing function that contributes to protection of the airway during the swallow. However, there is still an open question about the physiological source of swallowing vibrations. In this paper, we investigate the correlation between the swallowing vibrations recorded by the tri-axial accelerometer and hyoid bone kinetics observed in video-fluoroscopic swallow imaging studies. Further, this is a first of its kind study investigating this correlation with vibration signals measured in the medial-lateral plane of accelerometry. Our hypothesis is that there exists a correlation between the recorded swallowing vibrations in three axes and hyoid bone kinetics in videofluoroscopic images.Copyright © 2016 by ASME

Brain-computer interfaces (BCIs) promise to promote a novel access channel for functional independence for individuals with severe speech and physical impairment (SSPI) that can occur as a result of numerous neurological diseases and injuries. Current BCI systems lack the robustness and accuracy to allow individuals with SSPI to complete tasks required for independent living (e.g. communication or navigation). We aim to develop a noninvasive hybrid BCI relying on two imaging modalities: Electroencephalography (EEG) and functional transcranial Doppler sonography (fTCD). Such hybrid BCI is expected to be sufficiently robust and accurate to be operated in a real-life environment.

The power law in the frequency spectrum S(f)=1/f^β allows for a good representation of the various time evolutions and complex interactions of many physiological processes. The spectral exponent β can be interpreted as the degree of fractal characteristic which in turn makes it some sort of biomarker that gives an idea of the relative health of an individual. This thesis presents a thorough investigation of prediction of the fractal nature of the process with specific consideration given to experimentally measured gait stride interval time series. The goal is to consider the accuracy of several time series prediction methods such as the neural networks, regression trees and bagged regression trees learning method. To test these methods we simulated stride intervals time series as 1/f^β processes. This investigation is to complement previous analyses on predicting the process with which this study compared. It was shown as result of the research that the greatest number of points one can accurately predict is between five and fifteen using the regression tree, the feedforward neural network and the AR model.

The power law in the frequency spectrum S(f) = 1/fβ allows for a good representation of the various time evolution and complex interactions of many physiological processes. The spectral exponent β can be interpreted as the degree of fractal characteristic which in turn makes it some sort of biomarker that gives an idea of the relative health of an individual. The prediction of the 1/fβ time series can thus prove to be an asset in the medical field where forecasting the future health state of an individual can be important for rehabilitation purposes. The goal of this paper is to consider the accuracy of several time series prediction methods such as the neural networks, regression trees and bagged regression trees learning method. To test these methods we simulate stride intervals time series as 1/fβ processes. Our results show that the regression trees can accurately predict between five and fifteen points.

Wireless transcutaneous power transfer and communication has the potential to reduce the size of implantable medical devices, thereby reducing patient discomfort and minimizing the tissue area exposed to foreign material. Electromagnetic transmission mechanisms through tissue are determined by tissue structure and associated frequency-dependent tissue properties, which are significant in the design of wireless implantable medical devices. The purpose of this study was to investigate the effects of varying tissue dielectric properties on maximum power transfer to a subcutaneously implanted device in a paired electrode system designed for use in proximity to metallic orthopedic implants. The transcutaneous system including external and implanted electrode pairs was simulated at several radio frequencies (125 kHz, 1 MHz, 13.56 MHz, 403 MHz, and 915 MHz) while varying the dielectric properties of the tissue medium over a range of physiological values. Maximum power transfer was calculated to represent the best-case power gain across the range of tissue properties and frequencies, and greater achievable efficiencies were seen with higher quality factor as a function of the tissue properties. The results suggest that in the paired electrode system, utilization of capacitive coupling allows the system to function in proximity to metallic surfaces such as orthopedic implants. The results also suggest that higher power gains are possible through a choice of implant location based on expected tissue properties.

Acquiring swallowing accelerometry signals using a comprehensive sensing scheme may be a desirable approach for monitoring swallowing safety for longer periods of time. However, it needs to be insured that signal characteristics can be recovered accurately from compressed samples. In this paper, we considered this issue by examining the effects of the number of acquired compressed samples on the calculated swallowing accelerometry signal features. We used tri-axial swallowing accelerometry signals acquired from seventeen stroke patients (106 swallows in total). From acquired signals, we extracted typically considered signal features from time, frequency and time-frequency domains. Next, we compared these features from the original signals (sampled using traditional sampling schemes) and compressively sampled signals. Our results have shown we can obtain accurate estimates of signal features even by using only a third of original samples.

Wireless energy transfer is a broad research area that has recently become applicable to implantable medical devices. Wireless powering of and communication with implanted devices is possible through wireless transcutaneous energy transfer. However, designing wireless transcutaneous systems is complicated due to the variability of the environment. The focus of this review is on strategies to sense and adapt to environmental variations in wireless transcutaneous systems. Adaptive systems provide the ability to maintain performance in the face of both unpredictability (variation from expected parameters) and variability (changes over time). Current strategies in adaptive (or tunable) systems include sensing relevant metrics to evaluate the function of the system in its environment and adjusting control parameters according to sensed values through the use of tunable components. Some challenges of applying adaptive designs to implantable devices are challenges common to all implantable devices, including size and power reduction on the implant, efficiency of power transfer and safety related to energy absorption in tissue. Challenges specifically associated with adaptation include choosing relevant and accessible parameters to sense and adjust, minimizing the tuning time and complexity of control, utilizing feedback from the implanted device and coordinating adaptation at the transmitter and receiver.

Dr. Arindam Chakrabarty JSPS Post-doctoral Fellow Division of Forest and Biomaterials Science Graduate School of Agriculture Kyoto University, Kyoto 606-8502 Japan e-mail: arindamchakrabarty.vu@gmail.com Prof. Priyadarsi De Polymer Research Centre Department of Chemical Sciences Indian Institute of Science Education and Research Kolkata Mohanpur 741246 Nadia, West Bengal India e-mail: p_de@iiserkol.ac.in

Objective:Hot flashes are classic symptoms of menopause. Emerging data link hot flashes to cardiovascular disease (CVD) risk, yet whether hot flashes are related to brain health is poorly understood. We examined the relationship between hot flashes (measured via physiologic monitor and self-report) and white matter hyperintensities (WMH) among midlife women. Methods:Twenty midlife women (aged 40-60 y) without clinical CVD, with an intact uterus and ovaries, and not taking hormone therapy were recruited. Women underwent 24 hours of ambulatory physiologic and diary hot flash monitoring to quantify hot flashes; magnetic resonance imaging to assess WMH burden; 72 hours of actigraphy to quantify sleep; and a blood draw, questionnaires, and physical measures to quantify demographics and CVD risk factors. Tests of a priori hypotheses regarding relationships between physiologically monitored and self-reported wake and sleep hot flashes and WMH were conducted in linear regression models. Results:More physiologically monitored hot flashes during sleep were associated with greater WMH, controlling for age, race, and body mass index (&bgr; [SE] = 0.0002 [0.0001], P = 0.03]. Findings persisted after controlling for sleep characteristics and additional CVD risk factors. No relationships were observed for self-reported hot flashes. Conclusions:More physiologically monitored hot flashes during sleep are associated with greater WMH burden among midlife women without clinical CVD. Results suggest that the relationship between hot flashes and CVD risk observed in the periphery may extend to the brain. Future work should consider the unique role of sleep hot flashes in brain health.