Abstract
Recognition of epileptic electroencephalogram (EEG) signals is vital to epileptic seizure detection. Current research on seizure detection mostly focused on generalized seizure analysis. Compared with generalized seizures, childhood focal epilepsy generally originates in one hemisphere of the brain, and the seizure onset patterns vary from patient to patient, making it difficult for analysis. Meanwhile, the frequency, amplitude and rhythm of EEG activities in children of different ages are different, making childhood focal epilepsy detection challenging. In this paper, the channel correlation features (CCF) containing the regional scalp EEG cross correlations and auto-correlations are proposed for children focal epilepsy EEG representation. The spectrum features are also extracted to characterize EEGs in different frequency bands. Further, a convolutional autoencoder (CAE)-based deep feature learning and dimensionality reduction model is proposed for discriminative EEG frequency domain feature extraction. An early seizure detection framework for the childhood focal epilepsy based on the fused CAE and CCF EEG features is finally developed, and an ensemble classification model (ECM) is applied to solve the imbalance issue between ictal, interictal, and preictal. The performance is evaluated on the EEG dataset collected by the Children’s Hospital, Zhejiang University School of Medicine (CHZU). Experiments show that the proposed algorithm can reach to the highest accuracy of 93.57% for the early seizure detection in childhood focal epilepsy.
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Acknowledgements
This work was supported by the National Key Research and Development Program of China (2021YFE0100100, 2021YFE0205400), the National Natural Science Foundation of China (U1909209), the Natural Science Key Foundation of Zhejiang Province (LZ22F030002), the Open Research Projects of Zhejiang Lab (2021MC0AB04), and the Key Research and Development Program of Zhejiang Province (2020C03038).
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This study has been approved by the Children’s Hospital, Zhejiang University School of Medicine and registered in Chinese Clinical Trail Registry (ChiCTR1900028804). All patients gave their informed consent prior to their inclusion in the study.
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Jiang, T., Zhu, J., Hu, D. et al. Early seizure detection in childhood focal epilepsy with electroencephalogram feature fusion on deep autoencoder learning and channel correlations. Multidim Syst Sign Process 33, 1273–1293 (2022). https://doi.org/10.1007/s11045-022-00839-7
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DOI: https://doi.org/10.1007/s11045-022-00839-7