Abstract
In this paper, we are providing the application of options using empirical wavelet transform (EWT) and J48 decision tree model for electroencephalogram (EEG) signal classification. The features were extracted from EEG signal based on the empirical wavelet transform. Empirical wavelet transform (EWT) decomposes the EEG signal in the form of the intrinsic mode functions (IMFs) which is an AM–FM signal. The statistical values were extracted from the decomposed signals resulting in the EWT features. The extracted features were classified using classifiers J48 and KNN classifier. The proposed J48 model achieved higher accuracy rates than that of the KNN algorithm.
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Thilagaraj, M., Pallikonda Rajasekaran, M. (2018). Epileptic Seizure Mining via Novel Empirical Wavelet Feature with J48 and KNN Classifier. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_23
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DOI: https://doi.org/10.1007/978-981-10-7566-7_23
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