Statistics Papers

Document Type

Conference Paper

Date of this Version


Publication Source

29th International Conference on Machine Learning


Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Process—the multi-level clustering hierarchical Dirichlet Process (MLC-HDP)—that simultaneously clusters datasets on multiple levels. Our seizure dataset contains brain activity recorded in typically more than a hundred individual channels for each seizure of each patient. The MLC-HDP model clusters over channels-types, seizure-types, and patient-types simultaneously. We describe this model and its implementation in detail. We also present the results of a simulation study comparing the MLC-HDP to a similar model, the Nested Dirichlet Process and finally demonstrate the MLC-HDP’s use in modeling seizures across multiple patients. We find the MLC-HDP’s clustering to be comparable to independent human physician clusterings. To our knowledge, the MLCHDP model is the first in the epilepsy literature capable of clustering seizures within and between patients.



Date Posted: 27 November 2017