Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.

This paper aims to develop a UHF Gen 2 RFID system for transcutaneous operation for identifying and monitoring orthopedic implants. The major problems of using existing RFID antennas at UHF band for transcutaneous operation include possible interference with pacemakers and interference with other RFID systems working at the same band. To provide a solution for the above problems, this paper uses transcutaneous near field communication (TNFC) technology based on capacitive coupling between the reader and the tag. The reader and tag both have two electrodes and the energy and signal transmission occurs with the coupling between the electrodes. Both the reader and the tag are impedance matched for maximum power transmission efficiency. A reading range of 4.1 cm is achieved through pork skin using 30 dBm power from the reader. The proposed UHF RFID system is a feasible solution to provide high efficiency for transcutaneous operation while can eliminate an interference with other medical devices or RFID devices.

The Internet of Things describes multiple distributed systems where all (or most) everyday items include embedded systems in order to connect to the internet. This paradigm has the potential to revolutionize global industry and daily life. Healthcare is once such industry where the Internet of Things may provide great advantages to patients, care givers, and medical institutions. As the number of radio frequency emitters increases under this new paradigm public health and safety must also be taken into account. This paper explores the electromagnetic interference on implantable cardiac rhythm management devices caused by RFID interrogators. A standard electromagnetic compatibility test framework is proposed in order to diagnose the possibility of interference. Also, a mitigation method is proposed and tested. It is shown that the proposed method can reduce the incidence of clinically significant interference by nearly 60%.

The overall goal of this project is to develop a humane non-human primate model of traumatic spinal cord injury that will facilitate the development and evaluation of therapeutic interventions. The model utilizes neurophysiological techniques to identify the location of the upper motor neuron axons that innervate the lower motor neurons that control tail musculature. This facilitates the placement of a selective lesion that partially disconnects the upper and lower motor neuron supply to the musculature of the tail. An implanted transmitter quantitatively measures electromyography data from the tail. The preliminary data indicates that this model is feasible. The subject was able to tolerate the implantation of the transmitter, without adverse effects. As well, there was no limb impairment, bowel dysfunction or bladder dysfunction. The histopathologic and electromyographic features of the selective experimental lesion were similar to human spinal cord injury.

Functional transcrannial Doppler (fTCD) is used for monitoring the hemodynamics characteristics of major cerebral arteries. Its resting-state characteristics are known only when considering the maximal velocity corresponding to the highest Doppler shift (so called the envelope signals). Significantly more information about the resting-state fTCD can be gained when considering the raw cerebral blood flow velocity (CBFV) recordings. In this paper, we considered simultaneously acquired envelope and raw CBFV signals. Specifically, we collected bilateral CBFV recordings from left and right middle cerebral arteries using 20 healthy subjects (10 females). The data collection lasted for 15 minutes. The subjects were asked to remain awake, stay silent, and try to remain thought-free during the data collection. Time, frequency and time-frequency features were extracted from both the raw and the envelope CBFV signals. The effects of age, sex and body-mass index were examined on the extracted features. The results showed that the raw CBFV signals had a higher frequency content, and its temporal structures were almost uncorrelated. The information-theoretic features showed that the raw recordings from left and right middle cerebral arteries had higher content of mutual information than the envelope signals. Age and body-mass index did not have statistically significant effects on the extracted features. Sex-based differences were observed in all three domains and for both, the envelope signals and the raw CBFV signals. These findings indicate that the raw CBFV signals provide valuable information about the cerebral blood flow which can be utilized in further validation of fTCD as a clinical tool.

Swallowing accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.

Non–stationarity relates to the variation over time of the statistics of a signal. Therefore, signals from practical applications which are realizations of non–stationary processes are difficult to represent and to process. In this paper, we provide a comprehensive discussion of the asynchronous representation and processing of non–stationary signals using a time-frequency framework. Power consumption and type of processing imposed by the size of the devices in many applications motivate the use of asynchronous, rather than conventional synchronous, approaches. This leads to the consideration of non–uniform, signal–dependent level–crossing and asynchronous sigma delta modulator (ASDM) based sampling. Reconstruction from a non– uniform sampled signal is made possible by connecting the sinc and the Prolate Spheroidal Wave (PSW) functions — a more appropriate basis. Two decomposition procedures are considered. One is based on the ASDM that generalizes the Haar wavelet representation and is used for representing analog non–stationary signals. The second decomposer is for representing discrete non– stationary signals. It is based on a linear-chirp based transform that provides local time-frequency parametric representations based on linear chirps as intrinsic mode functions. Important applications of these procedures are the compression and processing of biomedical signals as it will be illustrated.

Asynchronous signal processing is an appropriate low-power approach for the processing of bursty signals typical in biomedical applications and sensing networks. Different from the synchronous processing, based on the Shannon-Nyquist sampling theory, asynchronous processing is free of aliasing constrains and quantization error, while allowing continuous-time processing. In this paper we connect level-crossing sampling with time-encoding using asynchronous sigma delta modulators, to develop an asynchronous decomposition procedure similar to the Haar transform wavelet decomposition. Our procedure provides a way to reconstruct bounded signals, not necessarily band-limited, from related zero-crossings, and it is especially applicable to decompose sparse signals in time and to denoise them. Actual and synthetic signals are used to illustrate the advantages of the decomposer.

Because of the need to control power consumption, in many biomedical applications asynchronous processing of the data is more appropriate. In this paper, we present a scale-based decomposition algorithm for analog signals similar to the wavelet decomposition. Our procedure uses asynchronous sigma delta modulators (ASDMs) to represent the amplitude of a signal using the zero-crossing times of a binary signal. Changing the zero-crossing times into random sequences of pulse widths, it can be shown to be equivalent to an optimal level-crossing sampler using local averages as the quantization levels. Applying the generation of multi-level signals from the output of ASDMs for different scale parameters we are able to obtain a decomposer that in a few stages provides a close representation of the signal. To illustrate the performance of the proposed decomposition, we consider its application to the representation of heart sounds.