Automatic drowsiness detection using electroencephalogram signal

Drowsiness refers to the state of being sleepy or the state of minimal concentration. It is characterised by a decrease in a person's memory capacity and brain information processing speed. These conditions cause hazards in the real-time working environment such as driving, monitoring power gen...

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Vydáno v:	Electronics letters Ročník 56; číslo 25; s. 1383 - 1386
Hlavní autoři:	Dutta, A, Kour, S, Taran, S
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 10.12.2020
Témata:	automatic drowsiness detection system band‐limited modes clustering variational mode decomposition CVMD optimum allocation sampling analyses nonhomogenous EEG signals drowsiness detection performance electroencephalogram signal electroencephalogram‐based automatic drowsiness detection method electroencephalography hazards least squares approximations medical signal processing minimal concentration neural nets neurophysiology nonstationary behaviour oscillatory mode characteristics patient health person power generation real‐time working environment signal classification Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications support vector machines electroencephalography nonstationary behaviour least squares approximations support vector machines electroencephalogram signal drowsiness detection performance patient health real-time working environment CVMD optimum allocation sampling analyses nonhomogenous EEG signals signal classification medical signal processing hazards person minimal concentration electroencephalogram-based automatic drowsiness detection method power generation clustering variational mode decomposition neurophysiology automatic drowsiness detection system band-limited modes neural nets oscillatory mode characteristics
ISSN:	0013-5194, 1350-911X, 1350-911X
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Popis
Shrnutí:	Drowsiness refers to the state of being sleepy or the state of minimal concentration. It is characterised by a decrease in a person's memory capacity and brain information processing speed. These conditions cause hazards in the real-time working environment such as driving, monitoring power generation and patient health etc. These hazards can be sidestepped by introducing the automatic drowsiness detection system. This Letter suggested the electroencephalogram (EEG)-based automatic drowsiness detection method. The clustering variational mode decomposition (CVMD) explores the non-stationary behaviour of EEG for drowsiness detection. In CVMD optimum allocation sampling analyses non-homogenous EEG signals and converts those into homogeneous EEG clusters. These clusters are then decomposed into band-limited modes. The oscillatory mode characteristics are extracted in terms of several features. These features are fed as input into the least-squares support vector machine classifier. The proposed method provides better drowsiness detection performance in comparison with the different methods using the same data set.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2697