Open Access
June 2016 Nonparametric multi-level clustering of human epilepsy seizures
Drausin F. Wulsin, Shane T. Jensen, Brian Litt
Ann. Appl. Stat. 10(2): 667-689 (June 2016). DOI: 10.1214/15-AOAS851


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?”


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Drausin F. Wulsin. Shane T. Jensen. Brian Litt. "Nonparametric multi-level clustering of human epilepsy seizures." Ann. Appl. Stat. 10 (2) 667 - 689, June 2016.


Received: 1 February 2013; Revised: 1 January 2015; Published: June 2016
First available in Project Euclid: 22 July 2016

zbMATH: 06625665
MathSciNet: MR3528356
Digital Object Identifier: 10.1214/15-AOAS851

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

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 2 • June 2016
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