Epilepsy identification based on VMD, RELIEFF algorithm and machine learning classification techniques

1Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Epilepsy identification is done by visual observation of electroencephalography (EEG) signals, which is more sensitive to bias and time consuming. In most of the previous research of epileptic seizure detection suffers from unsuitability and low power for processing large datasets. To eliminate aforementioned problems a computerized detection method is required to aid medical professionals. In this paper, a new technique is proposed to identify the epilepsy based on VMD, RELIEFF algorithm and machine learning approach. To investigate the proposed method performance a public EEG dataset is adopted from university hospital bonn, Germany. The technique starts with the VMD, which is used to extract the features from each EEG signal. And then RELIEFF algorithm is adopted to identify the best features. Finally to categorize the normal and epilepsy EEG signals a machine learning classification (ANN, KNN, and SVM) approach is used. The results demonstrate that the adopted method (VMD+RELIEFF+SVM) can achieve a better accuracy, shows that a commanding method to identification and classification of epileptic seizures.

Cite

CITATION STYLE

APA

Khaleelulla, S. E., & Rajesh Kumar, P. (2019). Epilepsy identification based on VMD, RELIEFF algorithm and machine learning classification techniques. International Journal of Recent Technology and Engineering, 8(3), 6180–6185. https://doi.org/10.35940/ijrte.C5519.098319

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free