Abstract
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.
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Acknowledgements
This research is supported in part by the National Natural Science Foundation of China (No. 61672070, 61572004, 61650201, 61771026, 81471770), the Key Project of Beijing Municipal Education Commission (Research and application on a coevolutionary model of visual perception and cognition), the Beijing Municipal Natural Science Foundation (No. 4182005) and the Science and Technology Planning Project of Qinghai Province (No. 2016-ZJ-Y04).
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Cui, S., Duan, L., Qiao, Y. et al. Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features. J Ambient Intell Human Comput 14, 15557–15572 (2023). https://doi.org/10.1007/s12652-018-1000-3
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DOI: https://doi.org/10.1007/s12652-018-1000-3