Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 545613, 12 pages
http://dx.doi.org/10.1155/2013/545613
Research Article

Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

Sun-Hee Kim,1 Christos Faloutsos,1 and Hyung-Jeong Yang2

1Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Department of Computer Science, Chonnam National University, Gwangju 500-757, Republic of Korea

Received 7 January 2013; Revised 18 March 2013; Accepted 27 March 2013

Academic Editor: Yiwen Wang

Copyright © 2013 Sun-Hee Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately.