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

arXiv: https://arxiv.org/abs/2111.06526 · HTML: https://mlsanity.com/paper/2111.06526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures](https://mlsanity.com/paper/2007.00898/) (2020)

## Built on by (most cited first, 1 of 1)

- [EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network](https://mlsanity.com/paper/2505.15203/) (2025)

## Abstract (arXiv)

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.