Using the study of EEG signals, the diagnosis of the brain disorders can be done. By means of a fuzzy KNN classifier, the epileptic seizures existence in EEG signalscan be detected by an effective method which is offered in this paper. Because ofthe abnormal electrical act of a collection of brain cells which is calledseizure, a disease named Epilepsy is caused as a result of temporary fluctuation in brain functions. The performance of the analysis is carried out in 3steps. Forthe EEG signal decompositionwithin delta, theta, alpha, beta and gamma sub bands, usage of biorthogonal discrete wavelet transform is carried out in the initial step. Fromevery sub band, the statistical characteristics are taken out in the succeeding step and the EEG signal classification i.e. epileptic seizure which occurs or not has been performedby fuzzy KNN classifier in the last step. On behalf of two various groups of EEG signals, thistechnique is applicable: 1) Healthy (Normal) EEG dataset; 2) epileptic datasetin the course of a seizure interval. The presence of epileptic seizure in EEG signals can be effectively detected using the proposed methodwhich is presented in the experimental results andmoreoveranacceptable precision is presented in detection.


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    Title :

    EEG signal classification using wavelet and fuzzy KNN classifier


    Contributors:

    Conference:

    ADVANCED TRENDS IN MECHANICAL AND AEROSPACE ENGINEERING: ATMA-2019 ; 2019 ; Bangalore, India


    Published in:

    Publication date :

    2021-02-16


    Size :

    7 pages





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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