TY - GEN
T1 - Imbalanced EEG Analysis Using One-Shot Learning with Siamese Neural Network
AU - Munia, Munawara Saiyara
AU - Hosseini, Seyyed Mohammadsaleh
AU - Nourani, Mehrdad
AU - Harvey, Jay
AU - Dave, Hina
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Epilepsy is a socially-stigmatizing chronic neurological condition. Limited availability of seizure Electroencephalogram (EEG) data makes the application of machine learning techniques for epileptic seizure detection very challenging. In this work, an efficient algorithmic procedure is proposed to facilitate the learning and classification of epileptic seizures from imbalanced EEG data. We designed an end-to-end architecture by combining local binary pattern with Siamese convolutional neural network. We used local binary pattern due to its capability to capture distinguishable morphological characteristics in the EEG signal. Siamese convolutional neural network was used since it can learn a similarity metric using an extremely small number of training samples for seizure episodes. With availability of a very small amount of training (seizure) samples, the effectiveness of the proposed method was verified by comparing the Siamese convolutional neural network with a baseline convolutional neural network. The proposed architecture outperforms the baseline model and achieves an average of 11.66% increase in F1-measure.
AB - Epilepsy is a socially-stigmatizing chronic neurological condition. Limited availability of seizure Electroencephalogram (EEG) data makes the application of machine learning techniques for epileptic seizure detection very challenging. In this work, an efficient algorithmic procedure is proposed to facilitate the learning and classification of epileptic seizures from imbalanced EEG data. We designed an end-to-end architecture by combining local binary pattern with Siamese convolutional neural network. We used local binary pattern due to its capability to capture distinguishable morphological characteristics in the EEG signal. Siamese convolutional neural network was used since it can learn a similarity metric using an extremely small number of training samples for seizure episodes. With availability of a very small amount of training (seizure) samples, the effectiveness of the proposed method was verified by comparing the Siamese convolutional neural network with a baseline convolutional neural network. The proposed architecture outperforms the baseline model and achieves an average of 11.66% increase in F1-measure.
KW - Convolutional Neural Network (CNN)
KW - Electroencephalogram (EEG)
KW - Epileptic Seizure Detection
KW - Local Binary Pattern (LBP)
KW - One-shot Learning
KW - Siamese Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85118134637&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118134637&partnerID=8YFLogxK
U2 - 10.1109/ICHI52183.2021.00015
DO - 10.1109/ICHI52183.2021.00015
M3 - Conference contribution
AN - SCOPUS:85118134637
T3 - Proceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021
SP - 4
EP - 12
BT - Proceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Healthcare Informatics, ISCHI 2021
Y2 - 9 August 2021 through 12 August 2021
ER -