Linked Life Data

8894391

Source:http://linkedlifedata.com/resource/pubmed/id/8894391

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
1997-1-29
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0169-2607
pubmed:author
pubmed:issnType
Print
pubmed:volume
51
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
51-73
pubmed:dateRevised
2005-11-16
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Non-linear algorithms for processing biological signals.
pubmed:affiliation
Biomedical Engineering Department, Polytechnic University, Milano, Italy. cerutti@icil64.cilea.it
pubmed:publicationType
Journal Article, Review