基于短时心电信号的疲劳驾驶检测算法

心率变异性分析是最常用的一种基于心电信号的疲劳驾驶检测方法.然而，该方法需要被检测信号时间足够长，且准确率较低.因此提出一种基于短时心电信号的疲劳驾驶检测算法.首先，按照30s的时长截取短时心电信号序列，利用差分阈值法确定R波位置，根据R-R间期差值大小剔除不合格的噪声样本;然后，计算R-R间期序列的时域/频域特征并与利用ImageNet数据集预训练的深度卷积神经网络模型提取的特征相结合;最后，设计了一种随机森林分类器并基于这些特征进行分类.结果表明，该算法在疲劳驾驶检测上具有良好的分类效果，平均准确率达到91%.因此，相较于心率变异性分析方法，本算法检测所需心电信号更短，且在准确率上具备显著优势.

Driver Drowsiness Detection Algorithm Using Short-Time ECG Signals

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.