Detection of epilepsy using discrete cosine harmonic wavelet transform-based features and neural network classifier Online publication date: Mon, 09-Mar-2020
by G.R. Kiranmayi; V. Udayashankara
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 32, No. 2, 2020
Abstract: Epilepsy is a neurological disorder caused by the sudden hyper activity in certain parts of the brain. Electroencephalogram (EEG) is the commonly used cost effective modality for the detection of epilepsy. This paper presents a method to detect epilepsy using discrete cosine harmonic wavelet transform (DCHWT) and a neural network classifier. DCHWT is a harmonic wavelet transform (HWT) based on discrete cosine transform (DCT), which is proved to be a spectral estimation technique with reduced bias is used in this work. The proposed method involves decomposition of EEG signals into DCHWT sub-bands, extraction of features from sub-bands and classification using an artificial neural network (ANN) classifier. The main focus of this study is the automatic detection of epilepsy from interictal EEG. This is still a challenge to the researchers as interictal EEG looks like normal EEG which makes the detection difficult. The proposed method is giving classification accuracy of 93.33% to 100% for various classes.
Online publication date: Mon, 09-Mar-2020
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org