Publication: Fractal dimension based electroencephalogram analysis of drowsiness patterns
3
0
Issued Date
2010
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-77954928777
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
ECTI-CON 2010 - The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Vol , No. (2010), p.497-500
Suggested Citation
Tantisatirapong S., Senavongse W., Phothisonothai M. Fractal dimension based electroencephalogram analysis of drowsiness patterns. ECTI-CON 2010 - The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. Vol , No. (2010), p.497-500. Retrieved from: https://hdl.handle.net/20.500.14740/7560
Author(s)
Abstract
As drowsiness is one of the prime causes of traffic accidents, monitoring drivers' drowsiness is an active safety-focused research which involves monitoring both physical and physiological changes. This paper aims to characterize a subject's drowsiness based on electroencephalogram (EEG) analysis. The two effective fractal dimension (FD) algorithms: the variance fractal dimension (VFD) and the detrended fluctuation analysis (DFA) were investigated to reveal these EEG patterns. EEG data were recorded from sixteen channels of four healthy male subjects aged 19-33 years. Our result demonstrated that the proposed algorithms feasibly recognized alertness and drowsiness of EEG waveforms.