Drowsiness detection based on wavelet analysis of ECG and pulse signals

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 o...

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Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 491 - 495
Main Authors Zhang, Aihua, Liu, Fenghua
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
Accuracy
drowsiness detection
ECG
Electrocardiography
Electroencephalography
Entropy
Interference
Noise
pulse signal
support vector machine (SVM)
Support vector machines
Vectors
Wavelet analysis
Wavelet packets
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Summary: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).
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6513058