Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach.

Xiaojin Li,Guo-Qiang Zhang,Shiqiang Tao,Licong Cui,Shirin Jamal-Omidi,Samden D Lhatoo,Yan Huang

doi:10.2196/17061

Abstract

BackgroundSudden 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.ObjectiveThis study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units.MethodsWe 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 evaluation method for assessing the performance of PGES detection algorithms.ResultsThe time distance–based evaluation showed that our approach achieved a 5-second tolerance-based positive prediction rate of 0.95 for artifact-free signals. For signals with different artifact levels, our prediction rates varied from 0.68 to 0.81.ConclusionsWe introduced a feature-based, random forest approach for automatic PGES detection using multichannel EEG recordings. Our approach achieved increasingly better time distance–based performance with reduced signal artifact levels. Further study is needed for PGES detection algorithms to perform well irrespective of the levels of signal artifacts.

Highlights

Epilepsy is one of the most common neurological disorders, and it affects an estimated 65 million people worldwide [1]
This study focused on identifying the first slow wave brain activity, that is, the onset of the first intermittent slow wave (ISW) activity, which indicates that the brain activity will gradually recover [12,14]
Overview The dataset used for this study comprises 116 EEG signal recordings from 84 patients with generalized tonic-clonic seizures (GTCS) in the Center for SUDEP Research (CSR) data repository, with Postictal generalized electroencephalogram (EEG) suppression (PGES) annotated by domain experts

Summary

Introduction

Background Epilepsy is one of the most common neurological disorders, and it affects an estimated 65 million people worldwide [1]. When seizure occurs, altered movement, expression, and levels of consciousness are often observed in the affected person. In a worst-case scenario, frequent seizures may predispose a person to sudden unexpected death in epilepsy (SUDEP) [4]. Among neurological events and conditions, SUDEP is second only to stroke in years of potential life lost, highlighting the importance and significance of this condition for public health [5]. 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. 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

Methods

Results

Discussion

Conclusion