A Mixed Self-Exciting Process to Model Epileptic Seizures

TL;DR

This paper introduces a Bayesian mixed Hawkes process model to analyze seizure clustering and heterogeneity in epilepsy, using three-year diary data from 407 patients.

Contribution

It develops a novel Bayesian mixed Hawkes process incorporating Weibull baseline and covariates to model seizure dynamics and individual differences.

Findings

Average time between primary and secondary seizures is 1.57 days.

Patients have an average of 2.20 seizures per cluster.

Omitting random effects biases intensity estimates.

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures affecting more than 70 million people worldwide. Often, an individual with epilepsy is more likely to experience subsequent seizures following an initial seizure, a process we call seizure clustering. Motivated by seizure diary data collected over three years from 407 individuals newly diagnosed with focal epilepsy in the Human Epilepsy Project (HEP), we propose a Bayesian mixed Hawkes process model that addresses seizure clustering and heterogeneity between individuals. In the Hawkes process, the intensity is accelerated each time an event occurs, through the composition of background and excitation intensity functions. The proposed model incorporates a Weibull baseline intensity to model a trend in background seizure rates over time, while the excitation process accounts for seizure clustering within individuals. We model heterogeneity among individuals by including covariates and random effects in both the background and excitation intensities. In the HEP study, the average time between primary and secondary seizures within an individual is 1.57 (95\% CrI: 1.43, 1.70) days, with an average of 2.20 (1.96, 2.47) seizures per cluster. We demonstrate that omitting random effects in the presence of heterogeneity leads to underestimation of the background intensity and overestimation of excitation rates.