Intelligent Biosignal Analysis Methods

• Autore: Jović Alan
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (256 p.)
• Soggetto topico: Information technology industries
• Soggetto non controllato: accelerometer; accuracy; Alzheimer's disease; autonomic nervous system; brain functional connectivity; classifiers; CNN; convolution neural network (CNN); covariate shift; cross-participant; DEAP; deep learning; disgust; drowsiness classification; drowsiness detection; EEG; EEG features; electrocardiogram; electrocardiography; electroencephalogram (EEG); electroencephalography (EEG); emotion; emotion recognition; emotional state; event-centered data segmentation; eye blinks rate; fall detection; fatigue detection; feature extraction; feature selection; frequency band fusion; galvanic skin response; heart rate; individual differences; inter-participant; inter-subject variability; k-fold validation; machine learning; mental workload; Mish; myocardial infarction; n/a; neural network-based refinement; non-local attention mechanism; non-stationarity; olfactory training; optimal shrinkage; phase-locked value (PLV); photoplethysmography (PPG); psychophysics; residual attention; residual network; sensitivity; signal quality index; skin conductance level; sleep stage scoring; sleep staging; smell; spatial transformer networks; stress; surgery image; T-end annotation; tSQI; wearable device; wearable sensors; window duration; wine sensory analysis
• Persona (resp. second.): JovićAlan
• Sommario/riassunto: 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.
• Titolo autorizzato: Intelligent Biosignal Analysis Methods
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557354803321
• Lo trovi qui: Univ. Federico II