Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach

JMIR Medical Informatics
Xiaojin LiLicong Cui

Abstract

Sudden unexpected death in epilepsy (SUDEP) is second only to stroke in neurological events resulting in years of potential life lost. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a period of suppressed brain activity often occurring after generalized tonic-clonic seizure, a most significant risk factor for SUDEP. Therefore, PGES has been considered as a potential biomarker for SUDEP risk. Automatic PGES detection tools can address the limitations of labor-intensive, and sometimes inconsistent, visual analysis. A successful approach to automatic PGES detection must overcome computational challenges involved in the detection of subtle amplitude changes in EEG recordings, which may contain physiological and acquisition artifacts. This study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units. We used a combination of temporal, frequency, wavelet, and interchannel correlation features derived from EEG signals to train a random forest classifier. We also constructed and applied confidence-based correction rules based on PGES state changes. Motivated by practical utility, we introduced a new, time distance-based...Continue Reading

References

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Citations

Feb 3, 2021·Epilepsia·Xiaojin LiSamden D Lhatoo
Dec 29, 2020·BMC Medical Informatics and Decision Making·Yejin KimJiajie Zhang
Dec 29, 2020·BMC Medical Informatics and Decision Making·Bishal LamichhaneXiaoqian Jiang
Dec 29, 2020·BMC Medical Informatics and Decision Making·Cong ZhuGuo-Qiang Zhang
Apr 20, 2021·Medical Hypotheses·A RabinovitchD Braunstein

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Methods Mentioned

BETA
feature extraction

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