A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction
Article
Article Title | A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Ra, Jee Sook (Author), Li, Tianning (Author), Li, Yan (Author), Ra, Jee S., Li, Tianning and Li, Yan |
Journal Title | Sensors |
Journal Citation | 21 (23) |
Article Number | 7972 |
Number of Pages | 13 |
Year | Dec 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s21237972 |
Web Address (URL) | https://www.mdpi.com/1424-8220/21/23/7972 |
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. |
Keywords | EEG channel selection; permutation entropy; K nearest neighbors (KNN); support vector machine (SVM); genetic algorithm (GA) |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460501. Data engineering and data science |
Byline Affiliations | School of Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7098/a-novel-permutation-entropy-based-eeg-channel-selection-for-improving-epileptic-seizure-prediction
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