Discrimination power of long-term heart rate variability measures for chronic heart failure detection.
The aim of this study was to investigate the discrimination power of standard long-term heart rate variability (HRV) measures for the diagnosis of chronic heart failure (CHF). The authors performed a retrospective analysis on four public Holter databases, analyzing the data of 72 normal subjects and...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 1; pp. 67 - 75 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104569765&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104569765 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2011 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104569765 NLM21203855 2010918999 10.1007/s11517-010-0728-5 NLM21203855 104569765 ppf: 67 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Discrimination power of long-term heart rate variability measures for chronic heart failure detection. aug: au: Melillo P Fusco R Sansone M Bracale M Pecchia L Melillo, Paolo Fusco, Roberta Sansone, Mario Bracale, Marcello Pecchia, Leandro affil: Department of Biomedical, Telecommunication and Electronic Engineering (DIBET), University of Naples Federico II, Naples, Italy sug: subj: Heart Failure Diagnosis Heart Rate Physiology Adult Aged Electrocardiography, Ambulatory Methods Female Heart Failure Physiopathology Human Male Middle Age Retrospective Design Signal Processing, Computer Assisted Young Adult Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: The aim of this study was to investigate the discrimination power of standard long-term heart rate variability (HRV) measures for the diagnosis of chronic heart failure (CHF). The authors performed a retrospective analysis on four public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, an exhaustive search of all possible combinations of HRV measures was adopted and classifiers based on Classification and Regression Tree (CART) method was developed, which is a non-parametric statistical technique. It was found that the best combination of features is: Total spectral power of all NN intervals up to 0.4 Hz (TOTPWR), square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) and standard deviation of the averages of NN intervals in all 5-min segments of a 24-h recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00 and 89.74%, respectively. The results are comparable with other similar studies, but the method used is particularly valuable because it provides an easy to understand description of classification procedures, in terms of intelligible "if … then …" rules. Finally, the rules obtained by CART are consistent with previous clinical studies. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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