Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment

• Published in: IEEE journal of biomedical and health informatics 2011-01, Vol.15 (1), p.40-46
• Main Authors: Pecchia, L, Melillo, P, Sansone, M, Bracale, M
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Area Under Curve; Artificial neural networks; Chronic Disease; Chronic heart failure (CHF); classification and regression tree (CART); Classification tree analysis; Data Mining; Electrocardiography - methods; Female; Hafnium; Heart failure; Heart Failure - diagnosis; Heart Failure - physiopathology; Heart Rate - physiology; Heart rate variability; heart rate variability (HRV); Humans; Male; Middle Aged; New York Heart Association (NYHA) classification; Power measurement; Regression Analysis; Reproducibility of Results; ROC Curve; Sensitivity; Signal Processing, Computer-Assisted
• ISSN: 1089-7771; 2168-2194; 1558-0032; 2168-2208
• DOI: 10.1109/TITB.2010.2091647

In this study, we investigated the discrimination power of short-term heart rate variability (HRV) for discriminating normal subjects versus chronic heart failure (CHF) patients. We analyzed 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features, we designed a classifier based on the classification and regression tree (CART) method, which is a nonparametric statistical technique, strongly effective on nonnormal medical data mining. The best subset of features for subject classification includes square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), total power, high-frequencies power, and the ratio between low- and high-frequencies power (LF/HF). The classifier we developed achieved sensitivity and specificity values of 79.3% and 100 %, respectively. Moreover, we demonstrated that it is possible to achieve sensitivity and specificity of 89.7% and 100 %, respectively, by introducing two nonstandard features ΔAVNN and ΔLF/HF, which account, respectively, for variation over the 24 h of the average of consecutive normal intervals (AVNN) and LF/HF. Our results are comparable with other similar studies, but the method we used is particularly valuable because it allows a fully human-understandable description of classification procedures, in terms of intelligible "if ... then ..." rules.