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In this paper, we propose a time-series stochastic model based on a scalemixture distribution with Markov transitions to detect epileptic seizures inelectroencephalography (EEG). In the proposed model, an EEG signal at each timepoint is assumed to be a random variable following a Gaussian distribution. Thecovariance matrix of the Gaussian distribution is weighted with a latent scaleparameter, which is also a random variable, resulting in the stochasticfluctuations of covariances. By introducing a latent state variable with aMarkov chain in the background of this stochastic relationship, time-serieschanges in the distribution of latent scale parameters can be representedaccording to the state of epileptic seizures. In an experiment, we evaluatedthe performance of the proposed model for seizure detection using EEGs withmultiple frequency bands decomposed from a clinical dataset. The resultsdemonstrated that the proposed model can detect seizures with high sensitivityand outperformed several baselines.

A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection