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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.

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 --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer’s disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- n/a --- Alzheimer's disease