文献类型：期刊文献

英文题名：Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning

作者：Chen, Lan-lan[1];Zhao, Yu[1];Zhang, Jian[1];Zou, Jun-zhong[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2015

卷号：42

期号：21

起止页码：7344

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20152600974546);WOS:【SCI-EXPANDED(收录号:WOS:000360772500008)】；

基金：This work is partly supported by National Natural Science Foundation of China (Nos. 61201124, 51407078) and Fundamental Research Funds for the Central Universities (WH1414022, WJ1313004-1). We acknowledge the assistance of Takenao Sugi at Saga University in experiment design and Masatoshi Nakamura for meaningful discussions. and helpful comments.

语种：英文

外文关键词：Drowsiness detection; Electroencephalogram (EEG); Eyelid movements; Wavelet decomposition; Nonlinear features; Extreme learning machine (ELM)

摘要：Physiological signals such as electroencephalogram (EEG) and electrooculography (EOG) recordings are very important non-invasive measures of detecting a person's alertness/drowsiness. Since EEG signals are non-stationary and present evident dynamic characteristics, conventional linear approaches are not highly successful in recognition of drowsy level. Furthermore, previous methods cannot produce satisfying results without considering the basic rhythms underlying the raw signals. To address these drawbacks, we propose a system for drowsiness detection using physiological signals that present four advantages: (1) decomposing EEG signals into wavelet sub-bands to extract more evident information beyond raw signals, (2) extraction and fusion of nonlinear features from EEG sub-bands, (3) fusion the information from EEGs and eyelid movements, (4) employing efficient extremely learning machine for status classification. The experimental results show that the proposed method achieves not only a high detection accuracy but also a very fast computation speed. The proposed algorithm can be further developed into the monitoring and warning systems to prevent the accumulation of mental fatigue and declines of work efficiency in many environments such as vehicular driving, aviation, navigation and medical service. (C) 2015 Elsevier Ltd. All rights reserved.