Discrimination power of long-term heart rate variability measures for chronic heart failure detection

• Published in: Medical & biological engineering & computing Vol. 49; no. 1; pp. 67 - 74
• Main Authors: Melillo, Paolo, Fusco, Roberta, Sansone, Mario, Bracale, Marcello, Pecchia, Leandro
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 2011; Springer-Verlag; Springer Nature B.V
• Subjects: Adult; Aged; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Chronic heart failure (CHF); Chronic illnesses; Classification and regression tree (CART); Computer Applications; Data mining; Datasets; Discriminant analysis; Electrocardiography; Electrocardiography, Ambulatory - methods; Female; Gender; Heart failure; Heart Failure - diagnosis; Heart Failure - physiopathology; Heart rate; Heart Rate - physiology; Heart rate variability (HRV); Human Physiology; Humans; Imaging; Investigations; Male; Middle Aged; Original Article; Radiology; Regression analysis; Retrospective Studies; Signal Processing, Computer-Assisted; Standard deviation; Studies; Young Adult; Chronic heart failure (CHF); Heart rate variability (HRV); Data-mining; Classification and regression tree (CART)
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-010-0728-5

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.