A novel permutation entropy-based EEG channel selection for improving epileptic seizure prediction

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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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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 02 Feb 2022 05:34
Last Modified: 01 Jun 2022 01:21
Uncontrolled Keywords: EEG channel selection; permutation entropy; K nearest neighbors (KNN); support vector machine (SVM); genetic algorithm (GA)
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
11 Medical and Health Sciences > 1109 Neurosciences > 110999 Neurosciences not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.3390/s21237972
URI: http://eprints.usq.edu.au/id/eprint/46862

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