Linked Life Data

2328253

Source:http://linkedlifedata.com/resource/pubmed/id/2328253

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1990-5-25
pubmed:abstractText
The fractal dimension of subsets of time series data can be used to modulate the extent of filtering to which the data is subjected. In general, such fractal filtering makes it possible to retain large transient shifts in baseline with very little decrease in amplitude, while the baseline noise itself is markedly reduced (Strahle, W.C. (1988) Electron. Lett. 24, 1248-1249). The fractal filter concept is readily applicable to single channel data in which there are numerous opening/closing events and flickering. Using a simple recursive filter of the form: Yn = w.Yn-1 + (1 - w)Xn, where Xn is the data, Yn the filtered result, and w is a weighting factor, 0 less than w less than 1, we adjusted w as a function of the fractal dimension (D) for data subsets. Linear and ogive functions of D were used to modify w. Of these, the ogive function: w = [1 + p(1.5-D)]-1 (where p affects the amount of filtering), is most useful for removing extraneous noise while retaining opening/closing events.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0006-3002
pubmed:author
pubmed:issnType
Print
pubmed:day
13
pubmed:volume
1023
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
305-11
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
1990
pubmed:articleTitle
Fractal filtering of channel data.
pubmed:affiliation
Department of Biology, Purdue University, School of Medicine, Indianapolis.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.