# A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models

arXiv: https://arxiv.org/abs/1808.03630 · HTML: https://mlsanity.com/paper/1808.03630/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography](https://mlsanity.com/paper/2207.01391/) (2022)
- [Low Latency Real-Time Seizure Detection Using Transfer Deep Learning](https://mlsanity.com/paper/2202.07796/) (2022)
- [Energy-Efficient Seizure Detection Suitable for low-power Applications](https://mlsanity.com/paper/2406.16948/) (2024)
- [Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection](https://mlsanity.com/paper/2410.19815/) (2024)

## Abstract (arXiv)

We propose a novel Coupled Hidden Markov Model to detect epileptic seizures in multichannel electroencephalography (EEG) data. Our model defines a network of seizure propagation paths to capture both the temporal and spatial evolution of epileptic activity. To address the intractability introduced by the coupled interactions, we derive a variational inference procedure to efficiently infer the seizure evolution from spectral patterns in the EEG data. We validate our model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate that our model outperforms three baseline approaches which rely on a classical detection framework. Our model also demonstrates the potential to localize seizure onset zones in focal epilepsy.