Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-net...

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Veröffentlicht in:	Electronics (Basel) 2020-01, Vol.9 (1), p.142
Hauptverfasser:	Seok, Woojoon, Yeo, Minsoo, You, Jiwoo, Lee, Heejun, Cho, Taeheum, Hwang, Bosun, Park, Cheolsoo
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Alertness Algorithms Artificial neural networks Biomarkers Cardiovascular disease Classification Decision making Deep learning Electrocardiography Electroencephalography Experiments Feature extraction Low level Machine learning Monitoring systems Neural networks Optimization Physiological responses Physiology Sleep
Online-Zugang:	Volltext
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