TY - GEN
T1 - Improving Alcoholism Diagnosis
T2 - 13th International Conference on Brain Informatics, BI 2020
AU - Rahman, Shelia
AU - Sharma, Tanusree
AU - Mahmud, Mufti
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
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.
UR - http://www.scopus.com/inward/record.url?scp=85092166669&partnerID=8YFLogxK
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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 -
