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

TL;DR

This paper introduces a new coupled Hidden Markov Model for detecting epileptic seizures in multichannel EEG data, capturing seizure propagation and improving detection accuracy.

Contribution

The paper presents a novel coupled Hidden Markov Model with variational inference for seizure detection and localization in EEG data, advancing beyond traditional methods.

Findings

Outperforms baseline detection approaches in accuracy.

Effectively captures seizure propagation dynamics.

Shows potential for localizing seizure onset zones.

Abstract

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.