Signal Processing Using Non-invasive Physiological Sensors

doi:10.3390/books978-3-0365-3719-1

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Signal Processing Using Non-invasive Physiological Sensors

Edited by

March 2022

222 pages

ISBN978-3-0365-3720-7 (Hardback)
ISBN978-3-0365-3719-1 (PDF)

https://doi.org/10.3390/books978-3-0365-3719-1

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This book is a reprint of the Special Issue Signal Processing Using Non-invasive Physiological Sensors that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions.

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Keywords

movement intention; brain–computer interface; movement-related cortical potential; neurorehabilitation; phonocardiogram; machine learning; empirical mode decomposition; feature extraction; mel-frequency cepstral coefficients; support vector machines; computer aided diagnosis; congenital heart disease; statistical analysis; convolutional neural network (CNN); long short-term memory (LSTM); emotion recognition; EEG; ECG; GSR; deep neural network; physiological signals; electroencephalography; Brain-Computer Interface; multiscale principal component analysis; successive decomposition index; motor imagery; mental imagery; neurorehabilitation; classification; hybrid brain-computer interface (BCI); home automation; electroencephalogram (EEG); steady-state visually evoked potential (SSVEP); eye blink; short-time Fourier transform (STFT); convolution neural network (CNN); human machine interface (HMI); rehabilitation; wheelchair; quadriplegia; Raspberry Pi; image gradient; AMR voice; Open-CV; image processing; acoustic; startle; reaction; response; reflex; blink; mobile; sound; stroke; EMG; brain-computer interface; myoelectric control; pattern recognition; functional near-infrared spectroscopy; brain–computer interface; z-score method; channel selection; region of interest; channel of interest; respiratory rate (RR); Electrocardiogram (ECG); ECG derived respiration (EDR); auscultation sites; pulse plethysmograph; biomedical signal processing; feature extraction; machine learning; feature selection and reduction; empirical mode decomposition; discrete wavelet transform; hypertension