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Brain Topography

, Volume 30, Issue 6, pp 724–738 | Cite as

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Original Paper

Abstract

High frequency oscillations (HFOs) are a promising biomarker of epileptic tissue, but detection of these electrographic events remains a challenge. Automatic detectors show encouraging results, but they typically require optimization of multiple parameters, which is a barrier to good performance and broad applicability. We therefore propose a new automatic HFO detection algorithm, focusing on simplicity and ease of implementation. It requires tuning of only an amplitude threshold, which can be determined by an iterative process or directly calculated from statistics of the rectified filtered data (i.e. mean plus standard deviation). The iterative approach uses an estimate of the amplitude probability distribution of the background activity to calculate the optimum threshold for identification of transient high amplitude events. We tested both the iterative and non-iterative approaches using a dataset of visually marked HFOs, and we compared the performance to a commonly used detector based on the root-mean-square. When the threshold was optimized for individual channels via ROC curve, all three methods were comparable. The iterative detector achieved a sensitivity of 99.6%, false positive rate (FPR) of 1.1%, and false detection rate (FDR) of 37.3%. However, in an eight-fold cross-validation test, the iterative method had better sensitivity than the other two methods (80.0% compared to 64.4 and 65.8%), with FPR and FDR of 1.3, and 49.4%, respectively. The simplicity of this algorithm, with only a single parameter, will enable consistent application of automatic detection across research centers and recording modalities, and it may therefore be a powerful tool for the assessment and localization of epileptic activity.

Keywords

HFO Epilepsy Ripple Electrocorticogram Depth electrode 

Notes

Acknowledgements

We gratefully thank R. Zelmann for sharing iEEG recordings and visual HFO markings. This research was financially supported by Royal Thai Government Fellowship awarded to K. Charupanit.

Supplementary material

10548_2017_579_MOESM1_ESM.m (9 kb)
Supplementary material 1 (M 9 KB)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Biomedical Engineering, Henry Samueli School of Engineering, 3120 Natural Sciences IIUniversity of CaliforniaIrvineUSA

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