Title
A method for the time-varying nonlinear prediction of complex nonstationary biomedical signals.
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.
Year
DOI
Venue
2009
10.1109/TBME.2008.2008726
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
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,Complexity,EEG,heart rate variability (HRV),local nonlinear prediction,nonlinear dynamics,nonstationary signals,surrogate data
Signal processing,Predictability,Nonlinear system,Computer science,Artificial intelligence,Basis function,Surrogate data,Chaotic,Linear approximation,Autoregressive model,Computer vision,Algorithm,Speech recognition
Journal
Volume
Issue
ISSN
56
2
1558-2531
Citations 
PageRank 
References 
4
0.54
1
Authors
3
Name
Order
Citations
PageRank
Luca Faes1476.08
Ki H Chon2314.25
Giandomenico Nollo39110.07