期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 36, 期 12, 页码 1327-1335出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2005.10.001
关键词
state space model; Kalman filter; frequency decomposition; autoregressive model; conditional heteroscedasticity; non-stationarity; anaesthesia
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.
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