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Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD

Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD

Virender Kumar Mehla, Ashish Kumar, Amit Singhal, Pushpendra Singh, Manjeet Kumar, Rama Subrahmanyam Komaragiri
Copyright: © 2020 |Pages: 16
ISBN13: 9781799821205|ISBN10: 179982120X|EISBN13: 9781799821229
DOI: 10.4018/978-1-7998-2120-5.ch005
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MLA

Mehla, Virender Kumar, et al. "Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD." Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering, edited by Dilip Singh Sisodia, et al., IGI Global, 2020, pp. 80-95. https://doi.org/10.4018/978-1-7998-2120-5.ch005

APA

Mehla, V. K., Kumar, A., Singhal, A., Singh, P., Kumar, M., & Komaragiri, R. S. (2020). Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD. In D. Sisodia, R. Pachori, & L. Garg (Eds.), Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering (pp. 80-95). IGI Global. https://doi.org/10.4018/978-1-7998-2120-5.ch005

Chicago

Mehla, Virender Kumar, et al. "Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD." In Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering, edited by Dilip Singh Sisodia, Ram Bilas Pachori, and Lalit Garg, 80-95. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2120-5.ch005

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Abstract

With the rapid innovation in the field of healthcare, various biomedical signals, namely, electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), play a crucial role for accurate measurement of various diseases such as cardiovascular diseases, brain disorders, etc. In the present work, an efficient method based on empirical mode decomposition (EMD) has been proposed to detect the epileptic activity. The present study is composed of three parts. In the first part, EMD is used to decompose the EEG signal into a set of amplitude modulated and frequency modulated components, referred to as intrinsic mode functions (IMFs). In the second part, features such as standard deviation, kurtosis, and Hjorth parameters have been extracted from various IMFs. In the last stage, the features are employed as inputs to support vector machine classifier for classification between non-seizure and seizure EEG signals. The simulation results show that the proposed scheme has attained better classification accuracy when compared to existing state-of-the-art methods.

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