Research Area:  Machine Learning
Epileptic seizure prediction has the potential to promote epilepsy care and treatment. However, the seizure prediction accuracy does not satisfy the application requirements. In this paper, a novel framework for seizure prediction is proposed by learning synchronization patterns. For better representation, bag-of-wave (BoWav) feature extraction is proposed for modeling synchronization pattern of electroencephalogram (EEG) signal. An interictal codebook and preictal codebook, representing the local segments, are constructed by a clustering algorithm. Within a period of EEG signal on all electrodes, local segments are projected onto the learned codebooks. The proposed feature expresses the synchronization pattern of EEG signal with the histogram feature. Moreover, extreme learning machine (ELM) is used to classify the sequence of features. Experiments are performed on the Kaggle seizure prediction challenge dataset and the CHB-MIT dataset. The experiment on the CHB-MIT achieves a sensitivity of 88.24% and a false prediction rate per hour of 0.25.
Keywords:  
Electroencephalogram (EEG) signal
Epileptic Seizure Prediction
Bag-Of-Wave Features
Machine Learning
Deep Learning
Author(s) Name:  Song Cui, Lijuan Duan, Yuanhua Qiao & Ying Xiao
Journal name:  Journal of Ambient Intelligence and Humanized Computing
Conferrence name:  
Publisher name:  Springer
DOI:  10.1007/s12652-018-1000-3
Volume Information:  
Paper Link:   https://link.springer.com/article/10.1007/s12652-018-1000-3