TY - GEN
T1 - Imbalance Learning Using Neural Networks for Seizure Detection
AU - Birjandtalab, Javad
AU - Jarmale, Vipul Nataraj
AU - Nourani, Mehrdad
AU - Harvey, Jay
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/20
Y1 - 2018/12/20
N2 - Around 1% of world's population suffer from epileptic seizures which can lead to injuries and even unexpected death. Making use of EEG signals, which are proven to be the best indicators of seizures, we aim to build an Artificial Neural Networks to classify seizure and non-seizure events. However, the limited availability of seizure events in the EEG data makes it difficult for the automatic classifiers in general to accurately classify seizure events. To improve this, we propose an imbalance learning approach to improve accuracy of highly imbalanced seizure dataset. Since each patient provides a different response to the seizure, we personalize the classification models in terms of training data and model parameters. The proposed imbalance learning method provides an average F-measure accuracy above 86% for Physionet MIT dataset.
AB - Around 1% of world's population suffer from epileptic seizures which can lead to injuries and even unexpected death. Making use of EEG signals, which are proven to be the best indicators of seizures, we aim to build an Artificial Neural Networks to classify seizure and non-seizure events. However, the limited availability of seizure events in the EEG data makes it difficult for the automatic classifiers in general to accurately classify seizure events. To improve this, we propose an imbalance learning approach to improve accuracy of highly imbalanced seizure dataset. Since each patient provides a different response to the seizure, we personalize the classification models in terms of training data and model parameters. The proposed imbalance learning method provides an average F-measure accuracy above 86% for Physionet MIT dataset.
KW - Artificial Neural Network
KW - EEG
KW - Epileptic Seizure
KW - Imbalance Ratio
KW - Imbalanced Learning
UR - http://www.scopus.com/inward/record.url?scp=85060870587&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060870587&partnerID=8YFLogxK
U2 - 10.1109/BIOCAS.2018.8584683
DO - 10.1109/BIOCAS.2018.8584683
M3 - Conference contribution
AN - SCOPUS:85060870587
T3 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
BT - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Y2 - 17 October 2018 through 19 October 2018
ER -