Name
Abstract | ||
|---|---|---|
Drowsiness is an important factor in a large number of accidents. While significant research has been done into the physiological mechanisms that underlie sleep and the wake state, the available methods to identify drowsiness had more difficulty in practical applications. An objective measurement to evaluate sleep-wake state was studied based on the hidden information among electrocardiogram (ECG) and pulse signals that might offer insights into the nature of sleep. Pulse transit time (PTT) and Wavelet entropy (WE) were computed for twenty sets data which come from self-designed experiments to distinguish the two different states of mental. A significant increase of PTT and decrease of WE ware correlated with the state of drowsiness, and both feature t-test results were p<;0.01, thus showing that these features have significant differences between awake and sleep state. Furthermore, the two characteristics can be recommended as objective indicators for distinguishing the human mental states. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/BMEI.2012.6513139 | BMEI |
Keywords | Field | DocType |
sleep-wake state,pulse transit time (ptt),electrocardiography,drowsiness,wavelet entropy,wavelet entropy (we),neurophysiology,wavelet transforms,sleep,feature t-test results,medical signal processing,ecg,accidents,physiological mechanisms,feature extraction,electrocardiogram signals,self-designed experiments,entropy,pulse signals,pulse transit time,human mental states | Computer vision,Neurophysiology,Pattern recognition,Computer science,Pulse Transit Time,Pulse (signal processing),Feature extraction,Speech recognition,Artificial intelligence,Wavelet entropy,Wavelet transform | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4673-1183-0 | 0 | 0.34 |
References | Authors | |
3 | 1 | |