首页|Evaluating a deep learning model for EEG categorization of alcoholic and non-alcoholic subjects
Evaluating a deep learning model for EEG categorization of alcoholic and non-alcoholic subjects
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NETL
NSTL
Abstract Detection of consumed alcohol in persons is quite a tough job as the convention devices based on odor are sometimes unreliable. Electroencephalography is a technique normally applied to measure the electrical activity of the brain; however, it proved to be useful for evaluation of subjects with alcohol addiction also. This paper presents a new automatic alcoholism detection system using a deep learning technique with a Convolution neural network (CNN) architecture of four convolutional layers with EEG connectivity. Preprocessing of the EEGs was therefore done to reduce the artifacts and noise. Then, the functional connectivity in the time-frequency domain was calculated from pre-processed EEGs of both non-alcoholic and alcoholic EEGs. EEG connectivity was measured in the frequency range from 1 to 45 Hz, with steps of 1 Hz. Subsequently, the 64 × 64 connectivity matrices were used as input for a deep learning model consisting of four convolutional layers through a CNN architecture. The experimental outcomes demonstrated that, for the EEG categorization of non-alcoholics/alcoholics, the proposed method performed a mean categorization accuracy of 99.51%, sensitivity of 99.68%, and specificity of 99.36% for the UCI-ML EEG database. This could have the potential to yield an excellent performance compared with current EEG methods for diagnosing alcoholism. The obtained outcomes have proven that the proposed CNN architecture with four convolution layers is suitable and efficient for clinical usage for alcohol addiction diagnosis. This approach should be further validated with other alcohol-dependence EEG databases.
NETL
NSTL
