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

doi:10.1109/ICTS.2017.8265600

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

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 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 language	English
Title of host publication	Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	19-24
Number of pages	6
ISBN (Electronic)	9781538628256
DOIs	https://doi.org/10.1109/ICTS.2017.8265600
Publication status	Published - 19 Jan 2018
Event	11th International Conference on Information and Communication Technology and System, ICTS 2017 - Surabaya, Indonesia Duration: 31 Oct 2017 → 31 Oct 2017

Publication series

Name	Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
Volume	2018-January

Conference

Conference	11th International Conference on Information and Communication Technology and System, ICTS 2017
Country/Territory	Indonesia
City	Surabaya
Period	31/10/17 → 31/10/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.1109/ICTS.2017.8265600

Cite this

Saddam, M., Tjandrasa, H., & Navastara, D. A. (2018). Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network. In Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017 (pp. 19-24). (Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTS.2017.8265600

Saddam, Muhammad ; Tjandrasa, Handayani ; Navastara, Dini Adni. / Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network. Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 19-24 (Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017).

@inproceedings{0a79b3cd88124ef2a395059c7dbca579,

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

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.",

keywords = "Alcoholism, EEG, Genetic Algorithm, Neural Network, Principal Component Analysis, Wavelet Packet Decomposition",

author = "Muhammad Saddam and Handayani Tjandrasa and Navastara, \{Dini Adni\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 11th International Conference on Information and Communication Technology and System, ICTS 2017 ; Conference date: 31-10-2017 Through 31-10-2017",

year = "2018",

month = jan,

day = "19",

doi = "10.1109/ICTS.2017.8265600",

language = "English",

series = "Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017",

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booktitle = "Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017",

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Saddam, M, Tjandrasa, H & Navastara, DA 2018, Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network. in Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017. Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 19-24, 11th International Conference on Information and Communication Technology and System, ICTS 2017, Surabaya, Indonesia, 31/10/17. https://doi.org/10.1109/ICTS.2017.8265600

Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network. / Saddam, Muhammad; Tjandrasa, Handayani; Navastara, Dini Adni.
Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 19-24 (Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network

AU - Saddam, Muhammad

AU - Tjandrasa, Handayani

AU - Navastara, Dini Adni

PY - 2018/1/19

Y1 - 2018/1/19

N2 - 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.

AB - 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.

KW - Alcoholism

KW - EEG

KW - Genetic Algorithm

KW - Neural Network

KW - Principal Component Analysis

KW - Wavelet Packet Decomposition

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U2 - 10.1109/ICTS.2017.8265600

DO - 10.1109/ICTS.2017.8265600

M3 - Conference contribution

AN - SCOPUS:85050282256

T3 - Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017

SP - 19

EP - 24

BT - Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th International Conference on Information and Communication Technology and System, ICTS 2017

Y2 - 31 October 2017 through 31 October 2017

ER -

Saddam M, Tjandrasa H, Navastara DA. Classification of alcoholic EEG using wavelet packet decomposition, principal component analysis, and combination of genetic algorithm and neural network. In Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 19-24. (Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017). doi: 10.1109/ICTS.2017.8265600

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

Abstract

Publication series

Conference

UN SDGs

Keywords

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