Modelling non-stationary variance in EEG time series by state space GARCH model

被引：34
|
作者：
Wong, Kin Foon Kevin
Galka, Andreas
Yamashita, Okito
Ozaki, Tohru
机构：
[1] Grad Univ Adv Studies, Minato Ku, Tokyo 1068569, Japan
[2] Univ Kiel, Inst Expt & Appl Phys, Kiel, Germany
来源：
COMPUTERS IN BIOLOGY AND MEDICINE | 2006年 / 36卷 / 12期
关键词：
state space model; Kalman filter; frequency decomposition; autoregressive model; conditional heteroscedasticity; non-stationarity; anaesthesia;
D O I：
10.1016/j.compbiomed.2005.10.001
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We present a new approach to modelling non-stationarity in EEG time series by a generalized state space approach. A given time series can be decomposed into a set of noise-driven processes, each corresponding to a different frequency band. Non-stationarity is modelled by allowing the variances of the driving noises to change with time, depending on the state prediction error within the state space model. The method is illustrated by an application to EEG data recorded during the onset of anaesthesia. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1327 / 1335
页数：9
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