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Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal.

Abstract：

BACKGROUND:Detecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features. NEW-METHOD:Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method. RESULTS:The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively. COMPARISON-WITH-EXISTING-METHOD:The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features. CONCLUSIONS:Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.

journal_name

J Neurosci Methods

journal_title

Journal of neuroscience methods

authors

B VP,Chinara S

doi

10.1016/j.jneumeth.2020.108927

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

108927

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(20)30350-2

journal_volume

347

pub_type

杂志文章

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Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal.