EEG Classification of Normal and Alcoholic by Deep Learning

Abstract

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.

Detection of Brain Abnormalities in Parkinson’s Rats by Combining Deep Learning and Motion Tracking

“…The figure shows that the accuracy and loss curves quickly converge, and the training accuracy and validation accuracy achieve a good fit, which proves that the model proposed in this paper has good robustness in this dataset. To verify the advantages of our proposed model, we compare the proposed model with the deep learning models in [27], [28], [29], [30], [31], [32], and [33] according to the model evaluation metrics, and both use our dataset for training. The comparison results are shown in Table IV.…”