Non-linear algorithms for processing biological signals

被引:29
作者
Cerutti, S
Carrault, G
Cluitmans, PJM
Kinie, A
Lipping, T
Nikolaidis, N
Pitas, I
Signorini, MG
机构
[1] UNIV RENNES,INSERM 9304,CJF,LAB TRAITEMENT SIGNAL & IMAGE,F-35042 RENNES,FRANCE
[2] EINDHOVEN UNIV TECHNOL,DEPT MED ELECT ENGN,NL-5600 MB EINDHOVEN,NETHERLANDS
[3] TAMPERE UNIV TECHNOL,SIGNAL PROC LAB,FIN-33101 TAMPERE,FINLAND
[4] ARISTOTELIAN UNIV THESSALONIKI,DEPT INFORMAT,GR-54006 THESSALONIKI,GREECE
关键词
median filtering; median learning vector quantizers; Wiener-Volterra kernel; non-linear dynamics; time-delay estimation; deterministic chaos;
D O I
10.1016/0169-2607(96)01762-2
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper illustrates different approaches to the analysis of biological signals based on non-linear methods. The performance of such approaches, despite the greater methodological and computational complexity-is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols. The methods introduced employ median filters for pattern recognition, adaptive segmentation, data compression, prediction and data modelling as well as multivariate estimators in data clustering through median learning vector quantizers. Another approach described uses Wiener-Volterra kernel technique to obtain a satisfactory estimation and causality test among EEG recordings. Finally, methods for the assessment of non-linear dynamic behaviour are discussed and applied to the analysis of heart rate variability signal. In this way invariant parameters are studied which describe non-linear phenomena in the modelling of the physiological systems under investigation.
引用
收藏
页码:51 / 73
页数:23
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