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- Title
Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG.
- Authors
Kim, Sun-Hee; Faloutsos, Christos; Yang, Hyung-Jeong
- 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 -1 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.
- Subjects
TREATMENT of epilepsy; AUTOREGRESSION (Statistics); MEDICAL forecasting; ELECTROENCEPHALOGRAPHY; TIME series analysis; COMPARATIVE studies
- Publication
Computational & Mathematical Methods in Medicine, 2013, p1
- ISSN
1748-670X
- Publication type
Article
- DOI
10.1155/2013/545613