ArticleNPJ digital medicine2021
Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Dual-Modal Wearable Pulse Detection System Integrated with Deep Learning for High-Accuracy and Low-Power Sleep Apnea Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Optimized robust learning framework based on big data for forecasting cardiovascular crises.Scientific reports · 2024Article
- The 2023 wearable photoplethysmography roadmap.Physiological measurement · 2023Review
- Denoising physiological data collected during multi-band, multi-echo EPI sequences.Aperture neuro · 2023Article
- Spectral analysis for pulse rate variability assessment from simulated photoplethysmographic signals.Frontiers in physiology · 2022Article
- A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree.Frontiers in physiology · 2022Article
- Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment.Sensors (Basel, Switzerland) · 2021Article
- PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals.Sensors (Basel, Switzerland) · 2021Article
- Relationship Between Heart Rate Variability and Pulse Rate Variability Measures in Patients After Coronary Artery Bypass Graft Surgery.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland-Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal-Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.
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Registered trials
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