Investigation of the Minimum Conditions for Reliable Estimation of Clinically Relevant HRV Measures - Introducing a Novel Approach to the Validation of HRV Measurement Systems

Abstract

The R-peak localization error (jitter) of a heart rate variability (HRV) system has a great impact on the values of the HRV measures. Only a few studies have analyzed this subject and purely done so from the aspect of choice of sampling frequency. In this study we provide an overview of the various factors that comprise the jitter of a system. We propose a method inspired by the field of signal averaged electrocardiography (SAECG) that allows for a quantification of the jitter of any HRV system that records and stores the raw ECG signal. Furthermore, with this method the differences between the HRV measures of the system and HRV measures corresponding to the physiological truth can be quantified. The method is used to obtain the physiologically true R-peak locations of subjects from Physionet's 'Normal Sinus Rhythm Database'. The effects of jitter are then analyzed via mathematical modelling for short-term and long-term HRV for various HRV measures. The effects of abnormal beats and missed and false detections are analyzed as well.

Towards Quasi-continuous Heart Rate Variability Estimation using a Patch Type Electrocardiogram Recorder

“…To investigate this, we compared HRV measures based on the automatically generated RR series with an estimate of the physiologically true HRV measures. The physiologically true HRV measures were estimated based on manual annotations of QRS complexes in 5-min ECG segments and a method recently designed by our group to accurately locate the physiological R peak position independently of the applied sampling frequency and bit resolution (Ahrens et al, 2015). : Schematic overview of the study design.…”

“…This might induce recording jitter in the HRV measures. Recently, our group has therefore designed a method to estimate the physiologically true R peak position independently of the applied sampling frequency (Ahrens et al, 2015). The input to the algorithm is an approximate QRS location.…”