Pulse Rate Variability Indices Research Articles

Experimental Verification of the Possibility of Reducing Photoplethysmography Measurement Time for Stress Index Calculation

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels. Traditional ultra-short-term stress measurement classifies stress situations using heart rate variability (HRV) or pulse rate variability (PRV) information extracted from electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, it requires more than one minute, making it difficult to monitor stress status in real-time and accurately predict stress levels. In this paper, stress indices were predicted using PRV indices acquired at different lengths of time (60 s, 50 s, 40 s, 30 s, 20 s, 10 s, and 5 s) for the purpose of real-time stress monitoring. Stress was predicted with Extra Tree Regressor, Random Forest Regressor, and Gradient Boost Regressor models using a valid PRV index for each data acquisition time. The predicted stress index was evaluated using an R2 score between the predicted stress index and the actual stress index calculated from one minute of the PPG signal. The average R2 score of the three models by the data acquisition time was 0.2194 at 5 s, 0.7600 at 10 s, 0.8846 at 20 s, 0.9263 at 30 s, 0.9501 at 40 s, 0.9733 at 50 s, and 0.9909 at 60 s. Thus, when stress was predicted using PPG data acquired for 10 s or more, the R2 score was confirmed to be over 0.7.

Duration of photoplethysmographic signals for the extraction of Pulse Rate Variability Indices

Pulse rate variability (PRV) assesses the changes in pulse rate through time when pulse rate is extracted from pulsatile signals such as the photoplethysmogram (PPG). PRV has been used as a surrogate of heart rate variability (HRV), but there is evidence of differences between these two variables. It has been hypothesised that these differences may arise from physiological processes or from technical aspects that may affect the reliable extraction of PRV indices from PPG signals. Moreover, there are no guidelines for the extraction of PRV information from pulsatile signals, which hinders the comparison among PRV studies and the understanding of physiological changes that may affect PRV. In this study, the effects of using PPG signals with different duration for the extraction of time-domain, frequency-domain and Poincaré plot indices from PRV was studied. Using simulated PPG signals with known PRV content and varying duration, it was found that PRV indices can be reliably estimated from signals as short as 90 s. This indicates that PRV indices can be extracted from ultra-short PPG signals. Although further validation with real data is needed, it can be concluded that acquiring shorter segments of PPG can be used for PRV analysis, allowing for a more efficient acquisition and processing of this variable.

Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study

Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences.

Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses.

Introduction: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown.Methods: To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE.Results: Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE.Conclusion: PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.

Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera.

Background Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method We took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms–0.06 ms, SDNN: 0.78 ms–0.46 ms, RMSSD: 1.79 ms–1.21 ms, and pNN50: 2.43%–1.63%. Conclusions Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.

Comparison between heart rate variability and pulse rate variability during different sleep stages for sleep apnea patients

The usefulness of heart rate variability (HRV) in the clinical research has been verified in numerous studies. However, it is controversy that using pulse rate variability (PRV) as a surrogate of HRV in different clinical applications. We aimed to investigate whether PRV extracted from finger pulse photoplethysmography (Pleth) signal could substitute HRV from ECG signal during different sleep stages by analyzing the common time-domain, frequency-domain and non-linear indices. Seventy-five sleep apnea patients were enrolled. For each patient, ECG and Pleth signals were simultaneously recorded for the whole night using Alice Sleepware Polysomnographic System and the sleep stage signals were automatically calculated by this System. Time-domain, frequency-domain and non-linear indices of both HRV and PRV were calculated for each sleep stage. Mann-Whitney U-test showed that for both time-domain and frequency-domain indices, there were no statistical differences between HRV and PRV results during all four sleep stages. For non-linear indices, sample entropy reported statistical differences between HRV and PRV results for N1, N2 and REM sleeps (all P< 0.01) whereas fuzzy measure entropy only reported statistical differences for REM sleep (P< 0.05). SDNN, LF and LF/HF indices decreased for both HRV and PRV with the sleep deepening while HF and non-linear indices increased. In addition, there were strong and significant correlation between HRV and PRV indices during all four sleep stages (all P< 0.01). PRV measurement could present the similar results as HRV analysis for sleep apnea patients during different sleep stages.

High natural killer cell number might identify stroke patients at risk of developing infections.

Objective:To investigate early changes in leukocyte subsets and autonomic function as predictors of the development of poststroke infections.Methods:We assessed the time course of leukocyte subsets in the blood of 59 patients with acute ischemic stroke. We divided the patients into 2 groups: those who developed infections during the first 7 days after stroke onset and those who did not. We measured urinary norepinephrine and epinephrine concentrations and pulse rate variability indices within 24 hours of admission.Results:We found that the number of circulating natural killer (NK) cells within the first hours after stroke was higher in stroke patients who developed infections (mean 435 cells/mL; 95% confidence interval [CI] 321–588) than in stroke patients who did not develop infections (mean 236 cells/mL; 95% CI 186–300; p = 0.001). This was followed by a decrease in all lymphocyte subsets from admission to day 1, varying between 22% and 40%, which was not seen in patients without poststroke infection (mean increase varied between 2% and 23%; all p < 0.005). In the group that developed infections, pulse rate variability revealed a decreased high frequency component. These findings all remained significant after adjustment for age and stroke volume.Conclusions:High circulating NK cell count within the first hours after ischemic stroke onset followed by a drop in all lymphocyte subsets identified patients who developed infections and may be caused by a sympathovagal imbalance with sympathetic overweight. These findings need to be validated in larger studies.

Evaluation of pulse rate variability obtained by the pulse onsets of the photoplethysmographic signal

This work presents the evaluation of pulse rate variability (PRV) obtained from pulse onsets of photoplethysmographic (PPG) signals. Three published algorithms were used to determine the pulse onsets: diastolic point, maximum second derivative and tangent intersection. Temporal series of pulse onsets were obtained for each method, and several variability indices were derived from these series. Simultaneous ECG and PPG records were acquired from 37 healthy volunteers to evaluate the interchangeability between PRV indices and heart rate variability (HRV) indices by the Bland–Altman method. Furthermore, the concordance correlation coefficient was used to correlate the indices. In all the cases, PRV indices obtained through the tangent intersection method showed better accuracy and precision (Bland–Altman analysis, bias ± 1.96 standard deviation: low frequency, LFms2 = −28.06 ± 72.68; high frequency, HFms2 = −68.23 ± 192.85; high frequency in normalized units, HFnu =−2.02 ± 7.08; LF/HF = 0.17 ± 0.71) and higher correlation (concordance correlation coefficients: low frequency, LFms2 = 0.99; high frequency, HFms2 = 0.98; high frequency in normalized units, HFnu = 0.97; LF/HF = 0.90) with HRV indices than other methods, and could be used as a good surrogate of HRV.

Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea

We investigate whether pulse rate variability (PRV) extracted from finger photo-plethysmography (Pleth) waveforms can be the substitute of heart rate variability (HRV) from RR intervals of ECG signals during obstructive sleep apnea (OSA). Simultaneous measurements (ECG and Pleth) were taken from 29 healthy subjects during normal (undisturbed sleep) breathing and 22 patients with OSA during OSA events. Highly significant ( p < 0.01) correlations (1.0 > r > 0.95) were found between heart rate (HR) and pulse rate (PR). Bland–Altman plot of HR and PR shows good agreement (<5% difference). Comparison of 2 min recording epochs demonstrated significant differences ( p < 0.01) in time, frequency domains and complexity analysis, between normal and OSA events using PRV as well as HRV measures. Results suggest that both HRV and PRV indices could be used to distinguish OSA events from normal breathing during sleep. However, several variability measures (SDNN, RMSSD, HF power, LF/HF and sample entropy) of PR and HR were found to be significantly ( p < 0.01) different during OSA events. Therefore, we conclude that PRV provides accurate inter-pulse variability to measure heart rate variability under normal breathing in sleep but does not precisely reflect HRV in sleep disordered breathing.

Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions

In this paper we assessed the possibility of using the pulse rate variability (PRV) extracted from the photoplethysmography signal as an alternative measurement of the HRV signal in non-stationary conditions. The study is based on analysis of the changes observed during a tilt table test in the heart rate modulation of 17 young subjects. First, the classical indices of HRV analysis were compared to the indices from PRV in intervals where stationarity was assumed. Second, the time-varying spectral properties of both signals were compared by time-frequency (TF) and TF coherence analysis. Third, the effect of replacing PRV with HRV in the assessment of the changes of the autonomic modulation of the heart rate was considered. Time-invariant HRV and PRV indices showed no statistically significant differences (p > 0.05) and high correlation (>0.97). Time-frequency analysis revealed that the TF spectra of both signals were highly correlated (0.99 ± 0.01); the difference between the instantaneous power, in the LF and HF bands, obtained from HRV and PRV was small (<10−3 s−2) and their temporal patterns were highly correlated (0.98 ± 0.04 and 0.95 ± 0.06 in the LF and HF bands, respectively) and TF coherence in the LF and HF bands was high (0.97 ± 0.04 and 0.89 ± 0.08, respectively). Finally, the instantaneous power in the LF band was observed to significantly increase during head-up tilt by both HRV and PRV analysis. These results suggest that although some differences in the time-varying spectral indices extracted from HRV and PRV exist, mainly in the HF band associated with respiration, PRV could be used as a surrogate of HRV during non-stationary conditions, at least during the tilt table test.