Mental fatigue recognition study based on 1D convolutional neural network and short-term ECG signals

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.