Comput Math Methods Med - Coercively adjusted auto regression model for forecasting in epilepsy EEG.

Tópicos

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Resumo

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.

Resumo Limpo

recent data complex characterist epilepsi electroencephalographi eeg time seri emerg epilepsi eeg data special characterist includ nonlinear nonnorm nonperiod therefor import find suitabl forecast method cover special characterist paper propos coerciv adjust autoregress caar method forecast futur valu multivari epilepsi eeg time seri use techniqu random coeffici forc adjust coeffici fractal dimens use determin order caar model appli caar method reflect special characterist data forecast futur valu epilepsi eeg data experiment result show compar previous method propos method can forecast faster accur

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