K-modes and Fuzzy C-means with modified Particle Swam Optimization Clustering Algorithm for Epilepsy Seizure Data
DOI:
https://doi.org/10.26438/ijcse/v7i1.7377Keywords:
Fuzzy C-means, K-mode, EEG, Seizures, Wavelet, PSO, ClusteringAbstract
Epilepsy is a stable neurological disorder of the brain, described by regular seizures, i.e., irregular activities. Seizure is the most imperative signal of epilepsy, which is solitary of the most expected neurological disorders. An electroencephalogram (EEG) is a test out used to weigh up the electrical activity in the brain, and is widely used in the recognition and study of epileptic seizures. Hence, it is decisive to develop a quantitative technique to automatically clustering the normal and epileptic brain activities. Several techniques have been developed for unbending out the important features of seizures present in EEGs. The proposed approach is evaluated an extracting the features of EEG signals using wavelet transform coefficients and unsupervised learning technique like clustering the data using Fuzzy C- Means with Modified Particle Swarm Optimization (PSO) and K- Mode Clustering. The recital of the Clusters are analyzed and examined that Fuzzy C-Means with PSO less error rate and out performs than K-Mode Clustering in accuracy.
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