Wang Hui, Zhang Pin, Jin Fenghu, Zhao Baoyong, Zeng Qinbo, Xiao Wendong
School of Automation, University of Science And Technology Beijing, Beijing 100083, P. R. China.
China Ordnance Equipment Group Automation Research Institute Co., Mianyang, Sichuan 621000, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Feb 25;41(1):34-40. doi: 10.7507/1001-5515.202306016.
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.
现代生活节奏在加快，生活压力逐渐增大，长期积累的精神疲劳对健康构成威胁。通过分析生理信号和参数，本文提出一种能够识别精神疲劳状态的方法，这有助于保持健康生活。本文提出的方法是一种基于卷积神经网络和长短期记忆的心电图信号心理疲劳状态的新识别方法。首先，利用一维卷积神经网络模型的卷积层提取局部特征，通过池化层提取关键信息，并去除一些冗余数据。然后，将提取的特征作为输入到长短期记忆模型中，进一步融合心电图特征。最后，通过全连接层整合关键信息，成功实现对精神疲劳状态的准确识别。结果表明，与传统机器学习算法相比，该方法将精神疲劳识别准确率显著提高到96.3%，为精神疲劳的预警和评估提供了可靠依据。
