  In this paper, we propose a time-series stochastic model based on a scale
mixture distribution with Markov transitions to detect epileptic seizures in
electroencephalography (EEG). In the proposed model, an EEG signal at each time
point is assumed to be a random variable following a Gaussian distribution. The
covariance matrix of the Gaussian distribution is weighted with a latent scale
parameter, which is also a random variable, resulting in the stochastic
fluctuations of covariances. By introducing a latent state variable with a
Markov chain in the background of this stochastic relationship, time-series
changes in the distribution of latent scale parameters can be represented
according to the state of epileptic seizures. In an experiment, we evaluated
the performance of the proposed model for seizure detection using EEGs with
multiple frequency bands decomposed from a clinical dataset. The results
demonstrated that the proposed model can detect seizures with high sensitivity
and outperformed several baselines.
