Approach based on wavelet packet transform and 1D-RMLBP for drowsiness detection using EEG

Early drowsiness detection may be crucial for the vehicle alertness system. Towards this, wearable technology, camera-based biophysical signals like electroencephalogram (EEG) approaches are utilised. In this Letter, the EEG-based approach is proposed to detect drowsiness. The proposed method consis...

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Vydáno v:	Electronics letters Ročník 56; číslo 25; s. 1378 - 1381
Hlavní autor:	Alçin, Ö.F
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 10.12.2020
Témata:	1D‐RMLBP camera‐based biophysical signals drowsiness detection electroencephalogram approaches electroencephalography entropy feature extraction k‐nearest neighbour classifier logarithmic energy entropy medical signal detection medical signal processing MIT‐BIH polysomnographic dataset nearest neighbour methods one‐dimensional region mean local binary pattern based feature extraction polysomnographic EEG signals random sampling‐based artificial signal augmentation signal classification Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications support vector machine classifiers support vector machines vehicle alertness system wavelet packet transform decomposition wavelet transforms wearable technology vehicle alertness system electroencephalography support vector machines wavelet transforms MIT-BIH polysomnographic dataset polysomnographic EEG signals wearable technology k-nearest neighbour classifier medical signal detection nearest neighbour methods support vector machine classifiers signal classification camera-based biophysical signals logarithmic energy entropy medical signal processing entropy electroencephalogram approaches wavelet packet transform decomposition feature extraction 1D-RMLBP random sampling-based artificial signal augmentation drowsiness detection one-dimensional region mean local binary pattern based feature extraction
ISSN:	0013-5194, 1350-911X, 1350-911X
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Shrnutí:	Early drowsiness detection may be crucial for the vehicle alertness system. Towards this, wearable technology, camera-based biophysical signals like electroencephalogram (EEG) approaches are utilised. In this Letter, the EEG-based approach is proposed to detect drowsiness. The proposed method consists of random sampling-based artificial signal augmentation, wavelet packet transform decomposition, logarithmic energy entropy, and one-dimensional region mean local binary pattern (1d-RMLBP) based feature extraction and classifier. k-Nearest neighbour and support vector machine classifiers are employed to detect the drowsiness. The MIT/BIH polysomnographic dataset has been used to test the proposed model. The proposed method has superior performance than the other methods using the same data set. The experimental results demonstrate that the proposed model could efficiently detect drowsiness from polysomnographic EEG signals.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2668