Authors: Shaohan Zhang; Zhenchang Zhang; Zelong Chen; Shaowei Lin; Ziyan Xie
Addresses: Key Laboratory of Smart Agriculture and Forestry, Fujian Agriculture and Forestry University, Fujian Province University, Fuzhou, Fujian Province 350002, China ' Key Laboratory of Smart Agriculture and Forestry, Fujian Agriculture and Forestry University, Fujian Province University, Fuzhou, Fujian Province 350002, China ' Medical Imaging Center, No. 900 Hospital of Joint Logistics Support Force, Fuzhou, Fujian Province, 350025, China ' Department of Epidemiology and Health Statistics, Public Health School, Fujian Medical University, Fuzhou, Fujian Province, 350100, China ' Key Laboratory of Smart Agriculture and Forestry, Fujian Agriculture and Forestry University, Fujian Province University, Fuzhou, Fujian Province 350002, China
Abstract: Mental fatigue is a state that may occur due to excessive work or long-term stress. Electroencephalography (EEG) is considered a reliable standard for mental fatigue detection. The existing EEG fatigue detection methods mainly use traditional machine learning models to classify mental fatigue after manual feature extraction. However, manual feature extraction is difficult and complicated. The quality of feature extraction largely determines the quality of the model. In this article, we collected EEG signals from 30 medical staff. The wavelet threshold denoising method was then applied to the measured EEG signal data to denoise the original EEG data, and a method based on a convolution and long short-term memory (CNN + LSTM) neural network to determine the fatigue state of medical staff. The extensive experiment on the established dataset clearly proves the advancement of our proposed algorithm compared to other neural network-based methods. Compared with the existing DNN, CNN and LSTM, the proposed model can quickly learn the information before and after the time series, so as to obtain higher classification accuracy.
Keywords: electroencephalography; EEG; mental fatigue detection; wavelet threshold denoising; fatigue scale; CNN; CNN + LSTM.
International Journal of Computational Science and Engineering, 2021 Vol.24 No.3, pp.290 - 300
Received: 08 Sep 2020
Accepted: 24 Nov 2020
Published online: 04 Jun 2021 *