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Epilepsy & Behavior
A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patientspecific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15-s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done using a support vector machine (SVM), whose outputs are averaged using a running 15-minute window todoi:10.1016/j.yebeh.2012.07.007 pmid:23041171 fatcat:rv54pyszyjgddikajwldrerluy