Key result
Random forest classifier achieves ~95% positive prediction rate for automatic PGES detection in artifact-free signals.
Why the study?
Visual analysis for detecting postictal generalized electroencephalogram suppression (PGES), a potential SUDEP risk biomarker, is labor-intensive and inconsistent, requiring automatic detection tools that can handle subtle amplitude changes and artifacts.
Answers are grounded in this paper and cite their sources.
A random forest machine learning approach can automatically detect postictal generalized EEG suppression with high accuracy in artifact-free signals.
BACKGROUND: 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. OBJECTIVE: This study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units. METHODS: 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 evaluation method for assessing the performance of PGES detection algorithms. RESULTS: The 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. CONCLUSIONS: We 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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Li et al. (2020) studied Epilepsy (Postictal generalized electroencephalogram suppression). Random forest approach for automatic PGES detection was evaluated on 5-second tolerance-based positive prediction rate. A random forest approach for automatic PGES detection achieved a 5-second tolerance-based positive prediction rate of 0.95 for artifact-free signals and 0.68 to 0.81 for signals with artifacts.
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