Date of this Version
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.
Wulsin, D., Jensen, S. T., & Litt, B. (2012). A Hierarchical Dirichlet Process Model With Multiple Levels of Clustering for Human EEG Seizure Modeling. 29th International Conference on Machine Learning, Retrieved from https://repository.upenn.edu/statistics_papers/103
Date Posted: 27 November 2017