Statistics Papers

Document Type

Journal Article

Date of this Version

2016

Publication Source

The Annals of Applied Statistics

Volume

10

Issue

2

Start Page

667

Last Page

689

DOI

10.1214/15-AOAS851

Abstract

Understanding neuronal activity in the human brain is an extremely difficult problem both in terms of measurement and statistical modeling. We address a particular research question in this area: the analysis of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures from a collection of patients. In these data, each seizure of each patient is defined by the activities of many individual recording channels. The modeling of epileptic seizures is challenging due the large amount of heterogeneity in iEEG signal between channels within a particular seizure, between seizures within an individual, and across individuals. We develop a new nonparametric hierarchical Bayesian model that simultaneously addresses these multiple levels of heterogeneity in our epilepsy data. Our approach, which we call a multi-level clustering hierarchical Dirichlet process (MLC-HDP), clusters over channel activities within a seizure, over seizures of a patient and over patients. We demonstrate the advantages of our methodology over alternative approaches in human EEG seizure data and show that its seizure clustering is close to manual clustering by a physician expert. We also address important clinical questions like “to which seizures of other patients is this seizure similar?”

Copyright/Permission Statement

The original and published work is available at: https://projecteuclid.org/euclid.aoas/1469199889

Keywords

Epilepsy, seizures, intracranial electroencephalogram (iEEG), Dirichlet process, nonparametric Bayes, clustering

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Date Posted: 27 November 2017

This document has been peer reviewed.