Abstract
Epileptic seizures may be described as the population entrainment of low complexity activities. Multistage seizure detection implemented using hidden Markov model (HMM) was proposed for the analysis of intracranial EEG recordings of epilepsy patients. The number of hidden states and the number of Gaussian clusters of the HMM were obtained using an unsupervised method. Multiple dynamic stages had been found leading to ictal activity. High sensitivity and specificity can be achieved based on receiver operating characteristic curve analysis (with area > 0.85). Spatial generalisation features study suggested that HMM framework is independent of the subject and the testing recording location.
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