Epileptic seizure detection by combining robust‐principal component analysis and least square‐support vector machine |
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| Authors: | Shanen Chen Xi Zhang Zhixian Yang |
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| Affiliation: | 1. Department of Industrial Engineering and Management, Peking University, Beijing, China;2. Department of Pediatrics, Peking University First Hospital, Beijing, China |
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| Abstract: | The feature extraction from electroencephalogram (EEG) signals is widely used for computer‐aided epileptic seizure detection. However, multiple channels of EEG signals and their correlations have not been completely harnessed. In this article, a novel automatic seizure detection approach is proposed by analyzing the spatiotemporal correlation of multi‐channel EEG signals. This approach combines the maximum cross‐correlation, robust‐principal component analysis, and least square‐support vector machine to detect the events. Our proposed method delivers higher detection sensitivity, specificity, and accuracy than the state‐of‐the‐art approaches based on the 19 channels’ EEG signals of 37 absence epilepsy patients experiencing 57 seizure events. |
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| Keywords: | electroencephalogram least square‐support vector machine maximum cross‐correlation robust‐principal component analysis seizure detection |
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