DocumentCode :
979258
Title :
A Method for the Time-Varying Nonlinear Prediction of Complex Nonstationary Biomedical Signals
Author :
Faes, Luca ; Chon, Ki H. ; Nollo, Giandomenico
Author_Institution :
Dept. of Phys., Univ. of Trento, Trento
Volume :
56
Issue :
2
fYear :
2009
Firstpage :
205
Lastpage :
209
Abstract :
A method to perform time-varying (TV) nonlinear prediction of biomedical signals in the presence of nonstationarity is presented in this paper. The method is based on identification of TV autoregressive models through expansion of the TV coefficients onto a set of basis functions and on k -nearest neighbor local linear approximation to perform nonlinear prediction. The approach provides reasonable nonlinear prediction even for TV deterministic chaotic signals, which has been a daunting task to date. Moreover, the method is used in conjunction with a TV surrogate method to provide statistical validation that the presence of nonlinearity is not due to nonstationarity itself. The approach is tested on simulated linear and nonlinear signals reproducing both time-invariant (TIV) and TV dynamics to assess its ability to quantify TIV and TV degrees of predictability and detect nonlinearity. Applicative examples relevant to heart rate variability and EEG analyses are then illustrated.
Keywords :
autoregressive processes; electroencephalography; medical signal processing; prediction theory; EEG; TV autoregressive models; TV deterministic chaotic signals; basis functions; complex nonstationary biomedical signals; heart rate variability; k -nearest neighbor local linear approximation; time-varying nonlinear prediction; Biological materials; Biophysics; Brain modeling; Chaos; Electroencephalography; Heart rate variability; Linear approximation; Predictive models; Signal analysis; TV; Vectors; Complexity; EEG; heart rate variability (HRV); local nonlinear prediction; nonlinear dynamics; nonstationary signals; surrogate data; Algorithms; Computer Simulation; Data Interpretation, Statistical; Electroencephalography; Heart Rate; Humans; Models, Biological; Nonlinear Dynamics; Predictive Value of Tests; Signal Processing, Computer-Assisted; Time Factors; Young Adult;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2008.2008726
Filename :
4667640
Link To Document :
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