LEADER 03574nam 2201117z- 450 001 9910557354803321 005 20231214133337.0 035 $a(CKB)5400000000042342 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76753 035 $a(EXLCZ)995400000000042342 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Biosignal Analysis Methods 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (256 p.) 311 $a3-0365-1692-1 311 $a3-0365-1691-3 330 $aThis 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. 606 $aInformation technology industries$2bicssc 610 $asleep stage scoring 610 $aneural network-based refinement 610 $aresidual attention 610 $aT-end annotation 610 $asignal quality index 610 $atSQI 610 $aoptimal shrinkage 610 $aemotion 610 $aEEG 610 $aDEAP 610 $aCNN 610 $asurgery image 610 $adisgust 610 $aautonomic nervous system 610 $aelectrocardiogram 610 $agalvanic skin response 610 $aolfactory training 610 $apsychophysics 610 $asmell 610 $awearable sensors 610 $awine sensory analysis 610 $aaccuracy 610 $aconvolution neural network (CNN) 610 $aclassifiers 610 $aelectrocardiography 610 $ak-fold validation 610 $amyocardial infarction 610 $asensitivity 610 $asleep staging 610 $aelectroencephalography (EEG) 610 $abrain functional connectivity 610 $afrequency band fusion 610 $aphase-locked value (PLV) 610 $awearable device 610 $aemotional state 610 $amental workload 610 $astress 610 $aheart rate 610 $aeye blinks rate 610 $askin conductance level 610 $aemotion recognition 610 $aelectroencephalogram (EEG) 610 $aphotoplethysmography (PPG) 610 $amachine learning 610 $afeature extraction 610 $afeature selection 610 $adeep learning 610 $anon-stationarity 610 $aindividual differences 610 $ainter-subject variability 610 $acovariate shift 610 $across-participant 610 $ainter-participant 610 $adrowsiness detection 610 $aEEG features 610 $adrowsiness classification 610 $afatigue detection 610 $aresidual network 610 $aMish 610 $aspatial transformer networks 610 $anon-local attention mechanism 610 $aAlzheimer's disease 610 $afall detection 610 $aevent-centered data segmentation 610 $aaccelerometer 610 $awindow duration 615 7$aInformation technology industries 700 $aJovic?$b Alan$4edt$01285484 702 $aJovic?$b Alan$4oth 906 $aBOOK 912 $a9910557354803321 996 $aIntelligent Biosignal Analysis Methods$93019592 997 $aUNINA