Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM

Bei Liu; Hongzi Bai; Wei Chen; Huaquan Chen; Zhen Zhang; Bei Liu; Hongzi Bai; Wei Chen; Huaquan Chen; Zhen Zhang

doi:10.3934/mbe.2023410

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 9349-9363. doi: 10.3934/mbe.2023410

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Research article Special Issues

Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM

1.
College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
2.
Hunan University of Arts and Science, Hunan Province Key Laboratory of Photoelectric Information Integration and Optical Manufacturing Technology, Changde 415000, China
3.
Furong College, Hunan University of Arts and Science, Changde 415000, China

Academic Editor: Zhichang Zhang

Received: 09 January 2023 Revised: 22 February 2023 Accepted: 28 February 2023 Published: 17 March 2023

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.
- epilepsy,
- EEG signals,
- improved refined composite multi-scale normalized dispersion entropy,
- particle swarm algorithm,
- support vector machine
Citation: Bei Liu, Hongzi Bai, Wei Chen, Huaquan Chen, Zhen Zhang. Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9349-9363. doi: 10.3934/mbe.2023410

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Abstract

Multi-scale dispersion entropy (MDE) has been widely used to extract nonlinear features of electroencephalography (EEG) signals and realize automatic detection of epileptic seizures. However, information loss and poor robustness will exist when MDE is used to measure the nonlinear complexity of the time sequence. To solve the above problems, an automatic detection method for epilepsy was proposed, based on improved refined composite multi-scale dispersion entropy (IRCMDE) and particle swarm algorithm optimization support vector machine (PSO-SVM). First, the refined composite multi-scale dispersion entropy (RCMDE) is introduced, and then the segmented average calculation of coarse-grained sequence is replaced by local maximum calculation to solve the problem of information loss. Finally, the entropy value is normalized to improve the robustness of characteristic parameters, and IRCMDE is formed. The simulated results show that when examining the complexity of the simulated signal, IRCMDE can eliminate the issue of information loss compared with MDE and RCMDE and weaken the entropy change caused by different parameter selections. In addition, IRCMDE is used as the feature parameter of the epileptic EEG signal, and PSO-SVM is used to identify the feature parameters. Compared with MDE-PSO-SVM, and RCMDE-PSO-SVM methods, IRCMDE-PSO-SVM can obtain more accurate recognition results.

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