Title :
Drowsiness detection based on wavelet analysis of ECG and pulse signals
Author :
Aihua Zhang ; Fenghua Liu
Author_Institution :
Coll. of Electr. & Inf. Eng., Lanzhou Univ. of Technol., Lanzhou, China
Abstract :
The purpose of this study is to explore the impact of drowsiness state on ECG and pulse signals and seek a more convenient and effective method for detecting drowsiness. Different frequency bands of ECG and pulse signals are selected to calculate the wavelet packet energy and wavelet entropy based on wavelet analysis. The results show that the wavelet packet energy of ECG whose frequency ranges are from 7.8Hz to 23.4Hz and from 23.4Hz to 62.5Hz respectively, and the wavelet entropy of pulse signal whose frequency range is from 0.1Hz to 31.25Hz are significantly decreased (p<;0.01) in the drowsiness state compared with that in the waking state. The accuracy rate of classification for these three features can reach 100% by using Support Vector Machines (SVM).
Keywords :
electrocardiography; feature extraction; medical signal processing; signal classification; sleep; support vector machines; wavelet transforms; ECG; SVM; drowsiness detection; drowsiness state; feature classification; frequency 23.4 Hz to 62.5 Hz; frequency 7.8 Hz to 23.4 Hz; frequency bands; pulse signals; support vector machines; waking state; wavelet analysis; wavelet entropy; wavelet packet energy; ECG; drowsiness detection; pulse signal; support vector machine (SVM); wavelet analysis;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-1183-0
DOI :
10.1109/BMEI.2012.6513058
