Evaluation of HRV-PRV agreement with autoencoder-based signal quality assessment in resting and dynamic conditions.

Objective.Pulse-to-pulse intervals obtained from continuous non-invasive blood pressure (CNBP) signals can be used to derive pulse rate variability (PRV). CNBP signals are susceptible to noise and motion artifacts, which can reduce the reliability of PRV and its agreement with heart rate variability (HRV) derived from electrocardiography (ECG). In this study, we applied a pulse-wise, unsupervised quality assessment method for CNBP signals based on a convolutional autoencoder integrating waveform morphology with pulse-level metadata. The aim was to improve the agreement between PRV and HRV metrics.Approach.Two datasets of CNBP and ECG recordings were analyzed: long baseline measurements (15 volunteers, average 40 min) and short recordings with postural changes (53 volunteers; 5 min squat-stand). A convolutional autoencoder was used for pulse-wise quality assessment. HRV metrics were derived from ECG, and PRV metrics were derived from CNBP before and after quality-based pulse selection (PRV). The analysis included standard time-domain indices (SDNN, RMSSD), frequency-domain measures, and nonlinear Poincaré plot descriptors (SD1, SD2). The agreement between HRV and PRV or PRVwas evaluated by Bland-Altman analysis.Main results.PRV systematically overestimated variability compared to HRV. During resting conditions, RMSSD and SDNN were lower for HRV (47.720.6 ms and 71.427.4 ms) than for PRV (61.726.7 ms,= 0.005 and 77.830.0 ms,= 0.003, respectively). After applying quality-based pulse selection, bias decreased fromtoms for RMSSD and fromtoms for SDNN, improving PRV-HRV agreement. During postural changes, PRV also overestimated HRV, but the effect of quality-based pulse selection was less pronounced.Significance.The proposed approach enhances CNBP signal quality without requiring manual labeling, making it suitable for real-world applications. Improved agreement between PRV and HRV may have important clinical implications, particularly when ECG acquisition is impractical or unavailable.