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Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals.

Hongyi Zhang1, Francisco H S Silva2, Elene F Ohata2,3

  • 1School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.

Frontiers in Human Neuroscience
|October 16, 2020
PubMed
Summary

Detecting alcoholism is challenging due to unreliable patient data. This study introduces an automated electroencephalogram (EEG) analysis using advanced computer vision, improving early alcoholism diagnosis accuracy.

Keywords:
alcoholismcomputer visionconvolutional neural networkelectroencephalogramtransfer learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Medical Diagnostics

Background:

  • Alcoholism diagnosis relies on patient self-reporting, which is often unreliable, hindering early detection and effective treatment.
  • Electroencephalogram (EEG) signals offer a more objective data source for assessing neural activity related to alcoholism.
  • Current diagnostic methods face challenges in accuracy and efficiency, necessitating novel approaches.

Purpose of the Study:

  • To propose and evaluate a novel, automated approach for diagnosing alcoholism using electroencephalogram (EEG) signal analysis.
  • To explore the utility of two-dimensional feature extraction and computer vision techniques, specifically Convolutional Neural Networks (CNNs), for EEG data.
  • To enhance the accuracy and reliability of early alcoholism detection through advanced computational methods.

Main Methods:

  • EEG signals were analyzed from a two-dimensional perspective, focusing on high and low-frequency neural activity changes.
  • A two-dimensional feature extraction method was employed, integrating Computer Vision (CV) techniques like Transfer Learning with CNNs.
  • The proposed method was evaluated using 21 traditional classification combinations and 84 CNN architectures as feature extractors, coupled with classifiers like SVM, k-NN, and Random Forest.

Main Results:

  • The combination of CNN MobileNet as a feature extractor and Support Vector Machine (SVM) as a classifier achieved the highest performance metrics.
  • This optimal model demonstrated excellent results: 95.33% Accuracy, 95.68% Precision, 95.24% F1-Score, and 95.00% Recall.
  • The proposed CNN-SVM approach outperformed traditional methods by up to 8%, indicating significant diagnostic improvement.

Conclusions:

  • The developed automated EEG analysis approach, utilizing CNNs and SVM, is highly effective for alcoholism diagnosis.
  • This method provides a reliable, computer-aided diagnostic tool suitable for patient triage and early clinical support.
  • The findings suggest a promising direction for improving the early detection and management of alcoholism.