Key result
Novel DWT-CNN-Bi-LSTM framework diagnoses alcoholism from EEG features with ~99% accuracy.
Why the study?
There is a lack of effective standard test procedures to detect alcoholism, and few studies exist on classifying alcohol EEG signals using deep learning models.
Does a deep learning method using CNN and Bi-LSTM improve the diagnostic accuracy of alcoholism from EEG signals compared to existing algorithms?
Does a deep learning method using CNN and Bi-LSTM improve the diagnostic accuracy of alcoholism from EEG signals compared to existing algorithms?
A novel deep learning approach combining CNN and Bi-LSTM can accurately classify EEG signals to diagnose alcoholism with 99.32% accuracy.
May aid automated EEG alcoholism screening; leaves open prospective validation before clinical use.
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.
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Li et al. (2022) studied Alcoholism (n=122). DWT-CNN-Bi-LSTM deep learning framework vs. Other machine learning and deep learning models was evaluated on Classification accuracy. A novel DWT-CNN-Bi-LSTM deep learning framework automatically extracted and classified EEG features to diagnose alcoholism with an accuracy of 99.32%.
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