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A particle swarm algorithm optimization-based SVM-KNN algorithm for epileptic EEG recognition
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DOI：10.1002/int.23040.png)
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Abstract
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Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (>= 95%) with only a small number of training samples, which has a good clinical application value.
Keywords:
empirical modal decomposition
epilepsy EEG recognition
K-nearest neighbor
particle swarm optimization algorithm
support vector machine
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