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Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study

Seedat, Zelekha A.; Rier, Lukas; Gascoyne, Lauren E.; Cook, Harry; Woolrich, Mark W.; Quinn, Andrew J.; Roberts, Timothy P. L.; Furlong, Paul L.; Armstrong, Caren; St. Pier, Kelly; Mullinger, Karen J.; Marsh, Eric D.; Brookes, Matthew J.; Gaetz, William


Zelekha A. Seedat

Harry Cook

Mark W. Woolrich

Andrew J. Quinn

Timothy P. L. Roberts

Paul L. Furlong

Caren Armstrong

Kelly St. Pier

Eric D. Marsh

William Gaetz


Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7± 2 mm (mean ±SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data-driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision-making for patients with intractable epilepsy.


Seedat, Z. A., Rier, L., Gascoyne, L. E., Cook, H., Woolrich, M. W., Quinn, A. J., …Gaetz, W. (2022). Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study. Human Brain Mapping,

Journal Article Type Article
Acceptance Date Sep 26, 2022
Online Publication Date Oct 19, 2022
Publication Date Oct 19, 2022
Deposit Date Oct 20, 2022
Publicly Available Date Oct 20, 2022
Journal Human Brain Mapping
Print ISSN 1065-9471
Electronic ISSN 1097-0193
Publisher Wiley
Peer Reviewed Peer Reviewed
Keywords Neurology (clinical); Neurology; Radiology, Nuclear Medicine and imaging; Radiological and Ultrasound Technology; Anatomy
Public URL
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