Abstract

Info

Title:: Classification of fatigue states based on short-term ECG signal and 1D-ECNN

Author(s):: WU Xue; WANG Raofen; School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Keywords:: fatigue state one-dimensional double convolutional neural network short-term electrocardiogram signal

Abstract:: One-dimensional double convolutional neural network (1D-ECNN) which includes 4 convolutional layers, 2 maximum pooling layers, 1 fully connected layer and 1 softmax output layer is proposed for detecting the fatigue state of the operator based on the acquired electrocardiogram (ECG) signals. Only a small number of convolutional cores are used in this study, which can reduce the number of model parameters, decrease the complexity of the model and increase the speed of model training, and meanwhile, it avoids the complicated feature extraction process or feature selection process in traditional methods. The acquired ECG signal is divided into samples with a time length of 1 s and then put into 1D-ECNN for classifying the fatigue state of the operator based on the short-term ECG signal. The simulation results show that the average classification accuracy of the proposed method is up to 95.72%, indicating that the proposed method can accurately detect the fatigue state of the operator in real time. In addition, it can better eliminate the effects of individual differences.