Cart (Loading....) | Create Account
Close category search window
 

Seizure detection using wavelet transform and a new statistical feature

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mihandoost, S. ; Dept. of Electr. Eng., Urmia Univ., Urmia, Iran ; Amirani, M.C. ; Varghahan, B.Z.

In this paper, we suggest a new set of statistic feature for the electroencephalogram (EEG) signals classification. We use two methods of seizure detection for evaluate new of statistic feature. Initially, features are extracted from EEG signals by using discrete wavelet transform. Next, a set of statistical features are extracted from each frequency sub-band to represent the distribution of wavelet coefficients. We suggest three new statistical features, fourth moment divided by second moment, difference between maximum and minimum, and zero-crossing of the wavelet coefficients. We demonstrate proposed features are very efficient for EEG classification and cause to improve correct classification rate (CCR). So, we use a linear discriminant analysis (LDA) and multilayer perceptron (MLP) for features selection. Next, the resultant data are applied to the classifiers. Two classifiers are employed: K-nearest neighbors (K-NN) and Bayesian. The data are classified into three categories: healthy volunteers, epilepsy patients during seizure-free interval and epilepsy patients during seizure. The experimental results indicate that performance of our method in EEG classification signals outperforms previously presented methods.

Published in:

Application of Information and Communication Technologies (AICT), 2011 5th International Conference on

Date of Conference:

12-14 Oct. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.