Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.

Telemonitoring systems equipped with photoplethysmography-based contour analysis of the digital arterial volume pulse (DVP) can be optimal tools for remote monitoring of cardiovascular patients; however, the method is known to be sensitive to errors. We aimed to show that DVP analysis is a reliable method to track cardiovascular status. We used our proprietary SCN4ALL telemedicine system and analyzed nine parameters derived from the DVP and its second derivative (SDDVP). First, we assessed the repeatability of system measurements by detecting artificial signals. Then test–retest reliability of human measurements was evaluated in healthy individuals under standardized conditions. The SCN4ALL system analyzed each parameter with high accuracy (coefficients of variation (CVs) < 1%). Test–retest reliability of most parameters (stiffness index, reflection index, left ventricular ejection time index, b/a, heart rate) was satisfactory (CVs < 10%) in healthy individuals. However, aging index and d/a ratio derived from the SDDVP were more variable. Photoplethysmography-based pulse contour analysis is a reliable method to monitor cardiovascular status if measurements are performed with a system of high accuracy. Our results highlighted that SDDVP parameters can be interpreted with limitations due to (patho)physiological variations of the DVP. We recommend the evaluation of these parameters only in measurements where all inflections of SDDVP are detected reliably.

Background: Cardiovascular (CV) mortality increases with age partly due to physiological ageing of the CV system. Early vascular ageing raises CV risks. Personalizing CV risk assessment by defining CV age could reduce CV events. Photoplethysmography (PPG), which analyses the peripheral arterial pulse wave, may be an effective method for estimating CV age. Ageing index (AGEi) and some other PPG parameters were proven to have age correlation; however, the age dependence of many other pulse wave parameters remains unclear. We aimed to identify age correlations of PPG indices and pulse rate variability (PRV) parameters including a few novel parameters which were calculated to further investigate the various aspects of ageing. Our study included 118 healthy (M/F: 53/65, mean age: 31.8 ± 11.8 SD) volunteers for PPG parameter calculation and 106 (M/F: 44/62, mean age: 32.6 ± 12.2 SD) for PRV parameters (age: 19–74). 2-min pulse wave recording was obtained using a pulse oximeter. An automated, proprietary software evaluated PPG and PRV parameter values, which were compared with chronological age (Pearson correlation and non-linear analysis). Results: PPG parameters describing various time-dependent aspects of cardiac ejection positively correlated with age, while those indicating arterial elasticity showed negative correlation. Composite PPG parameters proposed as indicators of CV health and fitness had negative, non-linear correlation. Most PRV parameters exhibited negative correlation, indicating reduced adaptive capacity due to ageing (p < 0.05, IrI > 0.3). Conclusions: PPG-based pulse waveform analysis provides a wide range of age-related parameters which display different patterns of age correlation, making it a promising method for estimating cardiovascular age. Future studies will include subjects with vascular ageing conditions beyond physiological values (e.g., hypertension, heart failure, coronary artery disease).