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
N1 - Publisher Copyright:
© 2017 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85050282256&partnerID=8YFLogxK
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 -