Abstract

Info

Title:: Classification of fatigue state using ECG signals and DCNN with transfer learning

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

Keywords:: Keywords: transfer learning short-term electrocardiogram signal fatigue classification two-dimensional image deep convolution neural network

Abstract:: Abstract: Although the traditional fatigue classification method based on electrocardiogram (ECG) signals can effectively identify fatigue state, it needs to acquire long-term ECG signals and cannot achieve the real-time monitoring of the fatigue state. A deep convolutional neural network is designed for evaluating the fatigue state of the operator, and automatically classifying the fatigue state based on the short-term ECG signals. A method of converting the ECG signals into an images are firstly proposed for converting the acquired ECG signals into a two-dimensional image, which means that the ECG signals are directly mapped to the two-dimensional space and converted into time-domain image information. Then the images are sent to deep convolutional neural network model for training, thereby realizing the classification of fatigue state. The proposed method decreases the complexity of the model and reduces the parameters of the model, and meanwhile, the training data does not need any preprocessing such as noise filtering and feature extraction steps. The results show that the proposed model can automatically extract effective features from the ECG signals, and realize the correct classification of the non-fatigue and fatigue states of the operator, achieving a classification accuracy up to 97.36%.