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FreSpeD: Frequency-Specific Change-Point Detection in Epileptic Seizure Multi-Channel EEG Data

Author:
Schröder, Anna Louise, Ombao, Hernando
Source:
Journal of the American Statistical Association 2019 v.114 no.525 pp. 115-128
ISSN:
1537-274X
Subject:
algorithms, brain, electroencephalography, equations, models, statistics
Abstract:
The goal in this article is to develop a practical tool that identifies changes in the brain activity as recorded in electroencephalograms (EEG). Our method is devised to detect possibly subtle disruptions in normal brain functioning that precede the onset of an epileptic seizure. Moreover, it is able to capture the evolution of seizure spread from one region (or channel) to another. The proposed frequency-specific change-point detection method (FreSpeD) deploys a cumulative sum-type test statistic within a binary segmentation algorithm. We demonstrate the theoretical properties of FreSpeD and show its robustness to parameter choice and advantages against two competing methods. Furthermore, the FreSpeD method produces directly interpretable output. When applied to epileptic seizure EEG data, FreSpeD identifies the correct brain region as the focal point of seizure and the timing of the seizure onset. Moreover, FreSpeD detects changes in cross-coherence immediately before seizure onset which indicate an evolution leading up to the seizure. These changes are subtle and were not captured by the methods that previously analyzed the same EEG data. Supplementary materials for this article are available online.
Agid:
6376158