03611nam 2201141z- 450 991055735480332120220111(CKB)5400000000042342(oapen)https://directory.doabooks.org/handle/20.500.12854/76753(oapen)doab76753(EXLCZ)99540000000004234220202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierIntelligent Biosignal Analysis MethodsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (256 p.)3-0365-1692-1 3-0365-1691-3 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.Information technology industriesbicsscaccelerometeraccuracyAlzheimer's diseaseautonomic nervous systembrain functional connectivityclassifiersCNNconvolution neural network (CNN)covariate shiftcross-participantDEAPdeep learningdisgustdrowsiness classificationdrowsiness detectionEEGEEG featureselectrocardiogramelectrocardiographyelectroencephalogram (EEG)electroencephalography (EEG)emotionemotion recognitionemotional stateevent-centered data segmentationeye blinks ratefall detectionfatigue detectionfeature extractionfeature selectionfrequency band fusiongalvanic skin responseheart rateindividual differencesinter-participantinter-subject variabilityk-fold validationmachine learningmental workloadMishmyocardial infarctionn/aneural network-based refinementnon-local attention mechanismnon-stationarityolfactory trainingoptimal shrinkagephase-locked value (PLV)photoplethysmography (PPG)psychophysicsresidual attentionresidual networksensitivitysignal quality indexskin conductance levelsleep stage scoringsleep stagingsmellspatial transformer networksstresssurgery imageT-end annotationtSQIwearable devicewearable sensorswindow durationwine sensory analysisInformation technology industriesJović Alanedt1285484Jović AlanothBOOK9910557354803321Intelligent Biosignal Analysis Methods3019592UNINA