Heart Rate n-Variability (HRnV): A Novel Representation of Beat-to-Beat Variation in Electrocardiogram

Abstract

Heart rate variability (HRV) is a widely adopted tool for evaluating changes in cardiac autonomic regulation. The majority of efforts have focused on developing methods to assess HRV by deriving sophisticated parameters with linear and nonlinear techniques and adopting advanced signal processing tools for efficient noise removal and accurate QRS detection. In this paper, we propose a novel representation of beat-to-beat variation in an electrocardiogram (ECG), called heart rate n-variability (HRnV), as an alternative to conventional HRV measures. We derived two novel HRnV measures based on non-overlapped and overlapped RR intervals. We also conducted a simulation study by using an ECG record from the MIT-BIH Normal Sinus Rhythm Database to demonstrate the feasibility of calculating HRnV parameters. Among the time domain parameters, we observed that the values were generally incremental with the increase in n. We observed the same trend of changes for the frequency domain parameters. In the nonlinear analysis, the differences between HRV and HRnV from Poincare plot measures were obvious, while those from entropy and detrended fluctuation analysis metrics were not. HRnV measures enable us to augment conventional HRV measures with additional parameters. Although issues remain to be addressed regarding HRnV, we hope to stimulate a new stream of research on this new representation of HRV. HRnV is an important addition to HRV and will contribute to extending the landscape of current studies on HRV.