Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal

Shelia Rahman; Tanusree Sharma; Mufti Mahmud

doi:10.1007/978-3-030-59277-6_22

Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal

Shelia Rahman, Tanusree Sharma^*, Mufti Mahmud

^*Corresponding author for this work

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

26 Scopus citations

Abstract

Alcoholism involves psychological and biological components where multiple risk factors come into play. Assessment of the psychiatric emergency is a challenging issue for clinicians working with alcohol-dependent patients. Identifying alcoholics from healthy controls from their EEG signals can be effective in this scenario. In this research, we have applied two instance-based classifiers and three neural network classifier to classify Electroencephalogram data of alcoholics and normal person. For data preprocessing, we have applied discrete wavelet transform, Principal component analysis and Independent component analysis. After successful implementation of the classifiers, an accuracy of 95% is received with Bidirectional Long Short-Term Memory. Finally, comparing the performance of the two categories of algorithms, we have found that neural networks have higher potentiality against instance-based classifiers in the classification of EEG signals of alcoholics.

Original language	English
Title of host publication	Brain Informatics - 13th International Conference, BI 2020, Proceedings
Editors	Mufti Mahmud, Stefano Vassanelli, M. Shamim Kaiser, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	239-250
Number of pages	12
ISBN (Print)	9783030592769
DOIs	https://doi.org/10.1007/978-3-030-59277-6_22
State	Published - 2020
Externally published	Yes
Event	13th International Conference on Brain Informatics, BI 2020 - Padua, Italy Duration: 19 Sep 2020 → 19 Sep 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12241 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Brain Informatics, BI 2020
Country/Territory	Italy
City	Padua
Period	19/09/20 → 19/09/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Alcoholism
EEG signal
Instance based classifier
Machine learning
Neural networks

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

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

Access to Document

10.1007/978-3-030-59277-6_22

Cite this

Rahman, S., Sharma, T., & Mahmud, M. (2020). Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal. In M. Mahmud, S. Vassanelli, M. S. Kaiser, & N. Zhong (Eds.), Brain Informatics - 13th International Conference, BI 2020, Proceedings (pp. 239-250). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12241 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59277-6_22

Rahman, Shelia ; Sharma, Tanusree ; Mahmud, Mufti. / Improving Alcoholism Diagnosis : Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal. Brain Informatics - 13th International Conference, BI 2020, Proceedings. editor / Mufti Mahmud ; Stefano Vassanelli ; M. Shamim Kaiser ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2020. pp. 239-250 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{bc9f13bc04ec4b9eab0e4b058f56848f,

title = "Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal",

abstract = "Alcoholism involves psychological and biological components where multiple risk factors come into play. Assessment of the psychiatric emergency is a challenging issue for clinicians working with alcohol-dependent patients. Identifying alcoholics from healthy controls from their EEG signals can be effective in this scenario. In this research, we have applied two instance-based classifiers and three neural network classifier to classify Electroencephalogram data of alcoholics and normal person. For data preprocessing, we have applied discrete wavelet transform, Principal component analysis and Independent component analysis. After successful implementation of the classifiers, an accuracy of 95\% is received with Bidirectional Long Short-Term Memory. Finally, comparing the performance of the two categories of algorithms, we have found that neural networks have higher potentiality against instance-based classifiers in the classification of EEG signals of alcoholics.",

keywords = "Alcoholism, EEG signal, Instance based classifier, Machine learning, Neural networks",

author = "Shelia Rahman and Tanusree Sharma and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 13th International Conference on Brain Informatics, BI 2020 ; Conference date: 19-09-2020 Through 19-09-2020",

year = "2020",

doi = "10.1007/978-3-030-59277-6\_22",

language = "English",

isbn = "9783030592769",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "239--250",

editor = "Mufti Mahmud and Stefano Vassanelli and Kaiser, \{M. Shamim\} and Ning Zhong",

booktitle = "Brain Informatics - 13th International Conference, BI 2020, Proceedings",

address = "Germany",

}

Rahman, S, Sharma, T & Mahmud, M 2020, Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal. in M Mahmud, S Vassanelli, MS Kaiser & N Zhong (eds), Brain Informatics - 13th International Conference, BI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12241 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 239-250, 13th International Conference on Brain Informatics, BI 2020, Padua, Italy, 19/09/20. https://doi.org/10.1007/978-3-030-59277-6_22

Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal. / Rahman, Shelia; Sharma, Tanusree; Mahmud, Mufti.
Brain Informatics - 13th International Conference, BI 2020, Proceedings. ed. / Mufti Mahmud; Stefano Vassanelli; M. Shamim Kaiser; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2020. p. 239-250 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12241 LNAI).

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

TY - GEN

T1 - Improving Alcoholism Diagnosis

T2 - 13th International Conference on Brain Informatics, BI 2020

AU - Rahman, Shelia

AU - Sharma, Tanusree

AU - Mahmud, Mufti

PY - 2020

Y1 - 2020

N2 - Alcoholism involves psychological and biological components where multiple risk factors come into play. Assessment of the psychiatric emergency is a challenging issue for clinicians working with alcohol-dependent patients. Identifying alcoholics from healthy controls from their EEG signals can be effective in this scenario. In this research, we have applied two instance-based classifiers and three neural network classifier to classify Electroencephalogram data of alcoholics and normal person. For data preprocessing, we have applied discrete wavelet transform, Principal component analysis and Independent component analysis. After successful implementation of the classifiers, an accuracy of 95% is received with Bidirectional Long Short-Term Memory. Finally, comparing the performance of the two categories of algorithms, we have found that neural networks have higher potentiality against instance-based classifiers in the classification of EEG signals of alcoholics.

AB - Alcoholism involves psychological and biological components where multiple risk factors come into play. Assessment of the psychiatric emergency is a challenging issue for clinicians working with alcohol-dependent patients. Identifying alcoholics from healthy controls from their EEG signals can be effective in this scenario. In this research, we have applied two instance-based classifiers and three neural network classifier to classify Electroencephalogram data of alcoholics and normal person. For data preprocessing, we have applied discrete wavelet transform, Principal component analysis and Independent component analysis. After successful implementation of the classifiers, an accuracy of 95% is received with Bidirectional Long Short-Term Memory. Finally, comparing the performance of the two categories of algorithms, we have found that neural networks have higher potentiality against instance-based classifiers in the classification of EEG signals of alcoholics.

KW - Alcoholism

KW - EEG signal

KW - Instance based classifier

KW - Machine learning

KW - Neural networks

UR - https://www.scopus.com/pages/publications/85092166669

U2 - 10.1007/978-3-030-59277-6_22

DO - 10.1007/978-3-030-59277-6_22

M3 - Conference contribution

AN - SCOPUS:85092166669

SN - 9783030592769

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 239

EP - 250

BT - Brain Informatics - 13th International Conference, BI 2020, Proceedings

A2 - Mahmud, Mufti

A2 - Vassanelli, Stefano

A2 - Kaiser, M. Shamim

A2 - Zhong, Ning

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 19 September 2020 through 19 September 2020

ER -

Rahman S, Sharma T, Mahmud M. Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal. In Mahmud M, Vassanelli S, Kaiser MS, Zhong N, editors, Brain Informatics - 13th International Conference, BI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 239-250. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59277-6_22

Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this