Ra, Jee Sook and Li, Tianning 
ORCID: https://orcid.org/0000-0001-5142-8654 and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926
(2021)
A novel permutation entropy-based EEG channel selection for improving epileptic seizure prediction.
Sensors, 21 (23):7972.
ISSN 1424-8220
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Text (Published Version)
sensors-21-07972-v2.pdf Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.
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