Intelligent Biosignal Analysis Methods

doi:10.3390/books978-3-0365-1691-2

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Hardback

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Keywords

sleep stage scoring; neural network-based refinement; residual attention; T-end annotation; signal quality index; tSQI; optimal shrinkage; emotion; EEG; DEAP; CNN; surgery image; disgust; autonomic nervous system; electrocardiogram; galvanic skin response; olfactory training; psychophysics; smell; wearable sensors; wine sensory analysis; accuracy; convolution neural network (CNN); classifiers; electrocardiography; k-fold validation; myocardial infarction; sensitivity; sleep staging; electroencephalography (EEG); brain functional connectivity; frequency band fusion; phase-locked value (PLV); wearable device; emotional state; mental workload; stress; heart rate; eye blinks rate; skin conductance level; emotion recognition; electroencephalogram (EEG); photoplethysmography (PPG); machine learning; feature extraction; feature selection; EEG; deep learning; non-stationarity; individual differences; inter-subject variability; covariate shift; cross-participant; inter-participant; drowsiness detection; EEG features; feature extraction; machine learning; drowsiness classification; fatigue detection; deep learning; residual network; Mish; spatial transformer networks; non-local attention mechanism; Alzheimer’s disease; fall detection; event-centered data segmentation; wearable sensors; accelerometer; window duration; n/a

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Intelligent Biosignal Analysis Methods

Edited by

August 2021

256 pages

ISBN 978-3-0365-1692-9 (Hardback)
ISBN 978-3-0365-1691-2 (PDF)

https://doi.org/10.3390/books978-3-0365-1691-2

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This is a Reprint of the Special Issue Intelligent Biosignal Analysis Methods that was published in

Computer Science & Mathematics

Summary

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.