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Intelligent Biosignal Analysis Methods
Alan Jović (editor)
2021
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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.
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Keywords
- 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
- Economics, finance, business & management
- EEG
- EEG features
- electrocardiogram
- Electrocardiography
- electroencephalogram (EEG)
- Electroencephalography (EEG)
- Émotion
- 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
- Industry & industrial studies
- Information technology industries
- inter-participant
- inter-subject variability
- k-fold validation
- Machine learning
- Media, information & communication industries
- 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
Links
DOI: 10.3390/books978-3-0365-1691-2Editions