ArticleJMIR medical informatics2020
Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.Epilepsia · 2024Trial
- Automated analysis of postictal generalized electroencephalogram suppression for SUDEP risk stratification.Epilepsia open · 2026Article
- Identification of Post-Ictal Generalised EEG Suppression with Two-Channel EEG.Sensors (Basel, Switzerland) · 2025Article
- Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree.Brain sciences · 2022Article
- A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection.Frontiers in neuroinformatics · 2022Article
- A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy.Frontiers in big data · 2022Article
- Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models.JMIR formative research · 2021Article
- Can Big Data guide prognosis and clinical decisions in epilepsy?Epilepsia · 2021Review
- Automated detection of activity onset after postictal generalized EEG suppression.BMC medical informatics and decision making · 2020Article
- A lightweight convolutional neural network for assessing an EEG risk marker for sudden unexpected death in epilepsy.BMC medical informatics and decision making · 2020Article
- A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.BMC medical informatics and decision making · 2020Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.