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Melillo, Paolo; Fusco, Roberta; Sansone, Mario; Bracale, Marcello; Pecchia, Leandro; University Federico II of Naples (2011)
Publisher: Springer
Languages: English
Types: Article
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).\ud We performed a retrospective analysis on 4 public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, we adopted an exhaustive search of all possible combinations of HRV measures and we developed classifiers based on Classification and Regression Tree (CART) method, which is a non-parametric statistical technique.\ud We 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-minute segments of a 24-hour recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00% and 89.74% respectively. \ud Our results are comparable with other similar studies, but the method we 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.
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