Intelligent Biosignal Processing in Wearable and Implantable Sensors

doi:10.3390/books978-3-0365-4602-5

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Intelligent Biosignal Processing in Wearable and Implantable Sensors

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June 2022

318 pages

ISBN978-3-0365-4601-8 (Hardback)
ISBN978-3-0365-4602-5 (PDF)

https://doi.org/10.3390/books978-3-0365-4602-5

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This book is a reprint of the Special Issue Intelligent Biosignal Processing in Wearable and Implantable Sensors that was published in

Biology & Life Sciences

Chemistry & Materials Science

Engineering

Summary

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

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Keywords

electrocardiogram; deep metric learning; k-nearest neighbors classifier; premature ventricular contraction; dimensionality reduction; classifications; Laplacian eigenmaps; locality preserving projections; compressed sensing; convolutional neural network; EEG; epileptic seizure detection; RISC-V; ultra-low-power; sepsis; atrial fibrillation; prediction; heart rate variability; feature extraction; random forest; annotations; myoelectric prosthesis; sEMG; grasp phases analysis; grasp classification; machine learning; electronic nose; liver dysfunction; cirrhosis; semiconductor metal oxide gas sensor; vagus nerve; intraneural; decoding; intrafascicular; recording; carbon nanotube; artificial intelligence; lens-free shadow imaging technique; cell-line analysis; cell signal enhancement; deep learning; compressed sensing; ECG signal; reconstruction dictionaries; projection matrices; signal classifications; osteopenia; sarcopenia; XAI; SHAP; IMU; gait analysis; artificial intelligence; sensors; convolutional neural networks; Parkinson’s disease; biomedical monitoring; accelerometer; pressure sensor; disease management; electromyography; correlation; high blood pressure; hypertension; photoplethysmography; electrocardiography; calibration; classification models; machine learning; deep learning; COVID-19; ECG trace image; transfer learning; Convolutional Neural Networks (CNN); feature selection; sympathetic activity (SNA); skin sympathetic nerve activity (SKNA); electrodes; electrocardiogram (ECG); cardiac time interval; dynamic time warping; fiducial point detection; heart failure; seismocardiography; wearable electroencephalography; motor imagery; motor execution; beta rebound; brain–machine interface; feature extraction; EEG classification; n/a