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

Hongyi Zhang; Francisco H S Silva; Elene F Ohata; Aldisio G Medeiros; Pedro P Rebouças Filho

doi:10.3389/fnhum.2020.00365

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

Front Hum Neurosci. 2020 Sep 18:14:365. doi: 10.3389/fnhum.2020.00365. eCollection 2020.

Authors

Hongyi Zhang¹, Francisco H S Silva², Elene F Ohata^{2

3}, Aldisio G Medeiros^{2

3}, Pedro P Rebouças Filho^{2

3

4}

Affiliations

¹ School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
² Laboratório de Processamento de Imagens, Sinais e Computação Aplicada, Instituto Federal do Ceará, Fortaleza, Brazil.
³ Programa de Pós-Graduação em Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza, Brazil.
⁴ Programa de Pós-Graduação em Ciência da Computação, Instituto Federal do Ceará, Fortaleza, Brazil.

Abstract

Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers: Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.

Keywords: alcoholism; computer vision; convolutional neural network; electroencephalogram; transfer learning.