Discrimination power of long-term heart rate variability measures

Abstract

Heart rate variability (HRV) can be assessed by time- or frequency-domain methods. The time-domain HRV measures are based on beat-to-beat intervals whereas frequency-domain analysis expresses HRV in terms of its constituent frequency components. HRV analysis has emerged as a diagnostic tool that quantifies the functioning of the anatomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several different time and frequency domain HRV measures together, which may be redundant and confusing in many cases. The question of which HRV measures are the strongest overall indicators of the cardiac condition has not been addressed. In this study, using data from 52 normal subjects and 22 patients with congestive heart failure, and linear discriminant analysis, we investigated the class, i.e. normal versus abnormal, discrimination power of 9 commonly used long-term HRV measures and identified the one that indicates the cardiac condition with higher sensitivity and specificity. Our results revealed that the standard deviation of all normal-to-normal beat intervals (SDNN) has the highest class discrimination power and a Bayesian classifier based on this index achieves sensitivity and specificity rates of 81.8% and 98.1% respectively.