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Classification of epilepsy period based on combination feature extraction methods and spiking swarm intelligent optimization algorithm
Author(s) -
Duan Lijuan,
Lian Zhaoyang,
Chen Juncheng,
Qiao Yuanhua,
Miao Jun,
Li Mingai
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5550
Subject(s) - epilepsy , computer science , robustness (evolution) , feature extraction , pattern recognition (psychology) , swarm behaviour , principal component analysis , artificial intelligence , electroencephalography , feature (linguistics) , algorithm , psychology , neuroscience , biochemistry , chemistry , linguistics , philosophy , gene
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.