Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network

Muhammad Saddam, Handayani Tjandrasa, Dini Adni Navastara

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

Alcoholism is a disorder characterized by excessive consumption and dependence on alcohol. There are various ways to detect whether a patient is addicted to alcohol, one of them by brain detection using electroencephalograph (EEG). However, the signals generated by the EEG recorder should be prepared to do further processing to detect brain abnormalities automatically. Therefore, this research implements Wavelet Packet Decomposition (WPD) method for feature extraction, Principal Component Analysis (PCA) for dimension reduction, and Back Propagation Neural Network optimized with Genetic Algorithm for alcohol addiction classification. Based on the experiment results, the best performance was 94.00% accuracy with decomposition of 3 levels, taking 30% of the features, and classification using Neural Network and Genetic Algorithm with learning rate of 0.1.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9781538628256
DOIs
Publication statusPublished - 19 Jan 2018
Event11th International Conference on Information and Communication Technology and System, ICTS 2017 - Surabaya, Indonesia
Duration: 31 Oct 201731 Oct 2017

Publication series

NameProceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
Volume2018-January

Conference

Conference11th International Conference on Information and Communication Technology and System, ICTS 2017
Country/TerritoryIndonesia
CitySurabaya
Period31/10/1731/10/17

Keywords

  • Alcoholism
  • EEG
  • Genetic Algorithm
  • Neural Network
  • Principal Component Analysis
  • Wavelet Packet Decomposition

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