Driver Drowsiness Detection Algorithm Using Short-Time ECG Signals

doi:10.12068/j.issn.1005-3026.2019.07.005

Abstract

Abstract: Heart rate variability analysis is used extensively for detecting driver drowsiness based on ECG signals. However， this method is deficient in accuracy and needs long-time ECG signal. An algorithm for driver drowsiness detection based on short-time ECG signals was proposed. First， the original ECG signal is rearranged into 30s segments， after which the R-wave positions are extracted using differential threshold algorithm and the noisy segments are excluded according to the calculated R-R interval. Then， time and frequency domains’ features of R-R interval series were extracted and combined with the features obtained by the deep convolutional neural network model with pre-trained weights of ImageNet dataset. Finally， random forest classifier was employed to detect the fatigue status of drivers based on the extracted features. The results demonstrate that the proposed algorithm has good performance in detecting driver drowsiness， with an averaged overall accuracy of 91%. The proposed algorithm needs shorter ECG signals and has higher accuracy in detecting driver drowsiness.

Key words: ECG signal, driver drowsiness, random forest, transfer learning, neural network

CLC Number:

N945.14

XU Li-sheng， ZHANG Wen-xu， PANG Yu-xuan， WU Cheng-yang. Driver Drowsiness Detection Algorithm Using Short-Time ECG Signals[J]. Journal of Northeastern University Natural Science, 2019, 40(7): 937-941.

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