Research Article Open Access

Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings

Hayat Sedrati1,2, Wajih Rhalem3,4, Nabil Aqili3, Chakib Nejjari5, Fatima El Omari6, Fatima El Omari7, Mostafa Belkasmi1, Abdellah Yousfi1,8 and Hassan Ghazal 4,9,10,11
  • 1 ICES Team, ENSIAS College, Mohammed V University in Rabat, Morocco
  • 2 National School of Public Health, Rabat, Morocco
  • 3 E2SN Research Team, ENSAM, Mohammed V University in Rabat, Morocco
  • 4 Moroccan Society of Digital Health, Rabat, Morocco
  • 5 Department of Epidemiology and Public Health, Faculty of Medicine, Pharmacy and Dental Medicine, University Sidi Mohammed Ben Abdellah, Fez, Morocco
  • 6 Arrazi University Psychiatric Hospital of Sale, Ibn Sina Hospital-University Center, Rabat, Morocco
  • 7 Medical School, Faculty of Medicine, Pharmacy and Dental Medicine, University Mohammed V in Rabat, Morocco
  • 8 Department of Economics, Faculty of Law Economics and Social Sciences-Souissi, Mohammed V University, Rabat, Morocco
  • 9 National Center for Scientific and Technical Research (CNRST), Rabat, Morocco
  • 10 Department of Laboratory of Genomics, Bioinformatics and Digital Health, Mohammed VI University of Health Sciences, Casablanca, Morocco
  • 11 Department of Genomics, Bioinformatics and Digital Health, Mohammed VI Center for Research and Innovation, Rabat, Morocco

Abstract

Alcoholism is a serious public health issue, and early diagnosis of this brain disease can be performed by analyzing Electroencephalogram (EEG) signals. However, the high dimensionality of EEG datasets requires significant computational time and resources for the automatic processing of EEG signals. This study proposes a novel method to reduce EEG dataset dimensionality using a Discrete to Continuous algorithm (DtC) by selecting optimal EEG channels. The DtC approach compares alcoholic and nonalcoholic EEG signals as two-time series in a two-dimensional space based on a distance measurement between the two-time series. The Dynamic Time Warping (DTW) algorithm is used to compare the performance of the DtC approach. Classification performance metrics were evaluated for both the DtC and DTW algorithms. The optimal selected channels by our approach are the C3, CP5, PO7, and F8 channels with accuracy values of 100, 100, 94 and 81%, respectively. These findings are consistent with previous research on statistical analysis and machine learning methods and with the DTW algorithm results. Our findings are also in line with scientific evidence from clinical research. The DtC approach was efficient in selecting the best channels to reduce the EEG dataset dimensionality, allowing us to select four out of the 64 EEG channels (C3, CP5, PO7, and F8) that retain essential information related to alcoholism, which is useful in reducing computational time and resources during the classification task of alcoholic EEG.

Journal of Computer Science
Volume 19 No. 1, 2023, 126-144

DOI: https://doi.org/10.3844/jcssp.2023.126.144

Submitted On: 15 November 2022 Published On: 17 January 2023

How to Cite: Sedrati, H., Rhalem, W., Aqili, N., Nejjari, C., Omari, F. E., Belkasmi, M., Yousfi, A. & Ghazal , H. (2023). Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings. Journal of Computer Science, 19(1), 126-144. https://doi.org/10.3844/jcssp.2023.126.144

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Keywords

  • Discrete to Continuous
  • Optimal Channel Selection
  • Alcoholism
  • Electroencephalography
  • Biological Time Series