Classification of epilepsy period based on combination feature extraction methods and spiking swarm intelligent optimization algorithm. (3rd January 2020)

• Record Type: Journal Article
• Authors: Duan, Lijuan; Lian, Zhaoyang; Chen, Juncheng; Qiao, Yuanhua; Miao, Jun; Li, Mingai
• Is Part Of: Concurrency and computation. Volume 33:Number 15(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-03
• Subjects: classification of epilepsy period -- combination feature extraction methods -- spiking swarm intelligent optimization -- time‐frequency feature; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5550
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Epilepsy seriously damages the physical and mental health of patients. Detection of epileptic EEG signals in different periods can help doctors diagnose the disease. The change of frequency components during epilepsy seizures is obvious, and there may be noises in epilepsy EEG signals. Moreover, epileptic seizures are closely related to the release of neuronal spiking in the brain. In this paper, we propose an approach for epilepsy period classification based on combination feature extraction methods and spiking swarm intelligent optimization classification algorithm. First, combination feature extraction methods take in account both the time‐frequency features and principal component features of epilepsy. The time‐frequency features are obtained by WPT or STFT‐PSD, and noises are removed while extracting principal component features by PCA. Second, spiking swarm intelligent optimization classification algorithm takes advantage of individual cooperation and information interaction with strong robustness. Its simulated neurons are closer to reality, which consider more information and obtain stronger computing power. The experimental results show that the average classification accuracy of the proposed method can reach 98.95% and the highest classification accuracy can reach 100%. Compared with other methods, the proposed method has the best classification performance.