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An Efficient Method for Classification of Alcoholic and Normal Electroencephalogram Signals Based on Selection of an
Maryam Dorvashi1,2, Neda Behzadfar1,2, Ghazanfar Shahgholian1,2
1Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Journal of Medical Signals and Sensors
|June 9, 2023
Summary
Katz fractal dimension (FD) in the FP2 channel of electroencephalogram (EEG) signals effectively distinguishes between alcoholic and normal brain patterns. This method offers a computationally efficient approach for accurate alcohol addiction diagnosis.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Alcohol addiction disrupts normal brain activity patterns.
- Electroencephalogram (EEG) signal analysis is a valuable tool for diagnosing and classifying alcohol-related neurological changes.
- Understanding these EEG signal alterations is crucial for developing effective diagnostic methods.
Purpose of the Study:
- To identify the most discriminative EEG features and channels for classifying alcoholic and normal subjects.
- To develop a computationally efficient method for diagnosing alcohol addiction using EEG signals.
- To evaluate the accuracy of different EEG features and channels in differentiating between alcoholic and non-alcoholic individuals.
Main Methods:
- One-second EEG signal segments were analyzed.
- Various frequency and non-frequency features, including power, permutation entropy (PE), approximate entropy (ApEn), Katz fractal dimension (katz FD), and Petrosian fractal dimension (Petrosion FD), were extracted.
- Statistical analysis and the Davis-Bouldin (DB) criterion were employed to select the most discriminative feature and EEG channel.
Main Results:
- The Katz fractal dimension (FD) in the FP2 channel demonstrated the highest discriminative capability between alcoholic and normal EEG signals.
- Classification accuracies of 98.77% and 98.5% were achieved using two different classifiers with 10-fold cross-validation.
- The FP2 channel and Katz FD were identified as the optimal combination for distinguishing alcohol-related EEG patterns.
Conclusions:
- The proposed method, utilizing Katz FD in the FP2 channel, enables accurate diagnosis of alcohol addiction with minimal features and channels.
- This approach offers low computational complexity, facilitating faster and more precise classification of alcoholic subjects.
- The findings support the potential of EEG-based analysis for objective and efficient alcohol addiction screening.
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