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E pileptic seizure detection by combining robust‐principal component analysis and least square‐support vector machine
Author(s) -
Chen Shanen,
Zhang Xi,
Yang Zhixian
Publication year - 2017
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22240
Subject(s) - electroencephalography , principal component analysis , pattern recognition (psychology) , computer science , support vector machine , correlation , artificial intelligence , feature extraction , sensitivity (control systems) , epilepsy , epileptic seizure , feature (linguistics) , speech recognition , mathematics , psychology , neuroscience , engineering , electronic engineering , geometry , linguistics , philosophy
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.