Li Houchi, Wu Lei
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China.
Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Hunan University of Science and Technology, Xiangtan 411100, China.
Brain Sci. 2022 Jun 14;12(6):778. doi: 10.3390/brainsci12060778.
Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol's EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG's features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms.
酒精依赖是一种在全球范围内常见的精神疾病。过量饮酒可能导致酒精中毒及许多并发症。在严重情况下，会导致呼吸和循环系统中枢的抑制和麻痹，甚至死亡。此外，缺乏有效的标准检测程序来检测酒精中毒。脑电图（EEG）信号是通过测量大脑皮层的脑变化获得的数据，可用于酒精中毒的诊断。现有的诊断方法主要采用机器学习技术，这些技术依赖人工干预来学习。相比之下，深度学习作为一种端到端的学习方法，可以自动提取脑电图信号特征，更加便捷。然而，使用深度学习模型对酒精脑电图信号进行分类的研究很少。因此，本文提出了一种新的深度学习方法来自动提取和分类脑电图特征。该方法首先采用多层离散小波变换对输入数据进行去噪。然后，将去噪后的数据作为输入，使用卷积神经网络和双向长短期记忆网络进行特征提取。最后，进行酒精脑电图信号分类。实验结果表明，本研究提出的方法可有效诊断酒精中毒患者，诊断准确率达到99.32%，优于目前大多数算法。
