A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling
This provides a novel method for analyzing epileptic seizure data across patients, which could aid in personalized treatment, though it is incremental in the context of hierarchical models.
The authors tackled the problem of clustering multi-level human intracranial EEG seizure data by introducing a new hierarchical Dirichlet Process variant (MLC-HDP) that simultaneously clusters channels, seizures, and patients, finding its clustering comparable to human physician clusterings.
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 MLC-HDP model is the first in the epilepsy literature capable of clustering seizures within and between patients.