Affiliation:
1. School of Life Sciences, Tiangong University, Tianjin, China
2. School of Electrical and Information Engineering, Tiangong University, Tianjin, China
3. Military Medical Examination and Certification Section, Chinese People’s Armed Police Force Specialty Medical Center, Tianjin, China
Abstract
BACKGROUND: Mental fatigue has become a non-negligible health problem in modern life, as well as one of the important causes of social transportation, production and life accidents. OBJECTIVE: Fatigue detection based on traditional machine learning requires manual and tedious feature extraction and feature selection engineering, which is inefficient, poor in real-time, and the recognition accuracy needs to be improved. In order to recognize daily mental fatigue level more accurately and in real time, this paper proposes a mental fatigue recognition model based on 1D Convolutional Neural Network (1D-CNN), which inputs 1D raw ECG sequences of 5 s duration into the model, and can directly output the predicted fatigue level labels. METHODS: The fatigue dataset was constructed by collecting the ECG signals of 22 subjects at three time periods: 9:00–11:00 a.m., 14:00–16:00 p.m., and 19:00–21:00 p.m., and then inputted into the 19-layer 1D-CNN model constructed in the present study for the classification of mental fatigue in three grades. RESULTS: The results showed that the model was able to recognize the fatigue levels effectively, and its accuracy, precision, recall, and F1 score reached 98.44%, 98.47%, 98.41%, and 98.44%, respectively. CONCLUSION: This study further improves the accuracy and real-time performance of recognizing multi-level mental fatigue based on electrocardiography, and provides theoretical support for real-time fatigue monitoring in daily life.