Pulse Rate Variability Parameters Research Articles

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

The oscillating pulse arrival time as a physiological explanation regarding the difference between ECG- and Photoplethysmogram-derived heart rate variability parameters

Background and ObjectiveECG-based heart rate variability (HRV), and more recently, optical pulse rate variability (PRV) analysis used in wearables are non-invasive tools of neurocardiac investigation. Pulse arrival time (PAT) is derived from their simultaneous recording. We examined the beat-to-beat PAT in greater detail, while analyzed the PRV to HRV relationship. MethodsWe acquired 300sec ECG, photoplethysmogram (PPG), and respiratory-signals from 35 young, healthy volunteers while in a supine position including different breathing patterns. PAT at eight reference points on the PPG-side was assessed by the mean, relative precision (RP%) and spectral analysis. PRV and HRV parameters were compared by relative accuracy error (RAE) and the Bland-Altman-Ratio (BAR). ResultsPAT showed the minimum RP% at the 1/2-amplitude point whereas RP% reached the maximum at the base point; its observed fine oscillation was associated to breathing as confirmed through spectral analysis. The instantaneous slope of PPG rise is inversely proportional to the corresponding PAT. RAE and BAR showed excellent agreement in 15 of 16 analysis at time-domain, mostly excellent or moderate on frequency-domain and nonlinear analysis. The inherent difference between HRV and PRV is formally due to the difference among two consecutive PATs. Conclusion and SignificanceThe present study supported the interchangeability of HRV- and PRV-analysis. Our formalistic explanation linking successive PAT-pairs and the inborn difference between HRV and PRV may bear further implications in selecting the appropriate fiducial points, approximating PRV to HRV measures, and in creating innovative neurocardiac parameters or complex PPG models/simulators.

Correlation of peripheral pulse wave features and pulse rate variability parameters with hospitalization of COVID-19 patients – the scientific aspect of a tele-pulse oximetry project study design

Abstract Introduction The Operational Group, leading the fight against COVID-19 in Hungary, has approved a pilot program, in December 2020, to monitor high risk COVID-19 patients in 40 family practices. The main goal of this project is to connect family physicians to their COVID-19 patients through a telemedicine system, in order to ensure patients' security and support for personalized telemedical-care and on-time hospitalization of the patients. 200 special pulse-oximeters were distributed and are continuously rotated among patients with acute phase of infection. Every patient is required to install a smartphone application on their phones to capture and transfer the oxygen saturation (SpO2), heart rate and raw pulse wave data. The peripheral pulse wave is used for calculating more than 30, cardiovascular and autonomic nervous system parameters by a proprietary algorithm. Most of these parameters are well described in the medical literature but not used for decision support in this project. Research question Do remote pulse wave analysis parameters have a predictive value on hospitalization above SpO2 recordings in COVID-19 patients? Methods Retrospective observational cohort study. The data analysis will be performed on the pulsewave database collected during the above mentioned project and correlated with endpoints. The parameters are measured with a special pulse oximeter; transferred, stored, processed and calculated by a remote patient monitor platform. The information about primary and secondary endpoints will be collected from the primary care physician of the patient. Study population Patients with diagnosed SARS-CoV-2 infection who have been monitored with a certain remote patient monitor system between November 2020 and September 2021. Expected number of participants: 100–150 patients. Primary endpoint Hospitalization due to acute COVID-19 infection Secondary endpoints Death connected to SARS-CoV-2 infection, presence of long-COVID symptoms during the monitoring period- maximum one month after the start of the infection Expected outcome According to literature data pulse wave features show correlation with cardiovascular status of the patient, while pulse rate variability parameters can be descriptive of the autonomic nervous system status of the subject. These parameters are described to possibly alter during and after an acute SARS-CoV-2 infection. To our knowledge there was no similar telemedical pulsewave data collection and analysis relation to COVID-19 before. However this is an easy to use methodology to explore more details about the patient's status. In this research we aim to explore these parameter's clinical utility in COVID-19 patients. Results will help to determine if pulse wave analysis and pulse rate variability parameters can be used as indicators of higher risk of hospitalization in COVID-19 patients. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): E-Med4All Europe Kft., Budapest, Hungary

Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor

Improved atrial fibrillation (AF) screening methods are required. We detected AF with pulse rate variability (PRV) parameters using a blood pressure device (BP+; Uscom, Sydney, Australia) and with a Kardia Mobile Cardiac Monitor (KMCM; AliveCor, Mountain View, CA). In 421 primary care patients (mean (range) age: 72 (31–99) years), we diagnosed AF (n = 133) from 12-lead electrocardiogram recordings, and performed PRV and KMCM measurements. PRV parameters detected AF with area under curve (AUC) values of up to 0.92. Using the mean of two sequential readings increased AUC to up to 0.94 and improved positive predictive value at a given sensitivity (by up to 18%). The KMCM detected AF with 83% sensitivity and 68% specificity. 89 KMCM recordings were “unclassified” or blank, and PRV detected AF in these with AUC values of up to 0.88. When non-AF arrhythmias (n = 56) were excluded, the KMCM device had increased specificity (73%) and PRV had higher discrimination performance (maximum AUC = 0.96). In decision curve analysis, all PRV parameters consistently achieved a positive net benefit across the range of clinical thresholds. In primary care, AF can be detected by PRV accurately and by KMCM, especially in the absence of non-AF arrhythmias or when combinations of measurements are used.

Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System.

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.

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.

Unintentional body movement parameters and pulse rate variability parameters are associated with the desire to void

Urinary incontinence is highly prevalent in elderly populations with physical and cognitive impairment. For the assessment and care of urinary incontinence, the desire to void is important. We have developed a bed sensor system that non-invasively and unconstrainedly measures the parameter changes of unintentional body movements. This study is aimed to evaluate the validity of measurement by the sensor system and parameters in healthy adults. We conducted experiments on 29 healthy adult volunteers. The parameters were unintentional body movement derived from changes in center of gravity and pulse rate variability (PRV) based on pulse wave measurements using a finger probe; further the relationship between the desire to void and measured parameters were examined. The body movement parameters at the buttock and thigh were associated with the desire to void (p < 0.050). All the PRV parameters trended significantly with desire to void as well (p < 0.050). The parameters achieved sensitivities of 0.18–0.88 in estimating strong desire to void, and 7 among 14 sensitivity measurements included in the receiver operating characteristic analysis exceeded 0.70. The body movement parameters and PRV parameters were useful in the estimation of the desire to void in healthy adults. To achieve accurate estimation, a combination of the PRV parameters and body movement parameters is required.

Towards pulse rate parametrization during free-living activities using smart wristband

Objective: The growing interest to integrate consumer smart wristbands in eHealth applications spawns the need for novel approaches of data parametrization which account for the technology-specific constraints. The present study aims to investigate the feasibility of a consumer smart wristband to be used for computing pulse rate parameters during free-living activities. Approach: The feasibility of computing pulse rate variability (PRV) as well as pulse rate and physical activity-related parameters using the smart wristband was investigated, having an electrocardiogram as a reference. The parameters were studied on the pulse rate and step data from 54 participants, diagnosed with various cardiovascular diseases. The data were acquired during free-living activities with no user lifestyle intervention. Main results: The comparison results show that the smart wristband is well-suited for computing the mean interbeat interval and the standard deviation of the averaged interbeat intervals. However, it is less reliable when estimating frequency domain and nonlinear parameters. Heart recovery time, estimated by fitting an exponential model to the events, satisfying the conditions of the 3 min step test, showed satisfactory agreement (relative error <20%) with the reference ECG in one-third of all cases. On the other hand, the heart’s adaptation to physical workload, expressed as the slope of the linear regression curve, was underestimated in most cases. Significance: The present study demonstrates that pulse rate parametrization using a consumer smart wristband is in principle feasible. The results show that the smart wristband is well suited for computing basic PRV parameters which have been reported to be associated with poorer health outcomes. In addition, the study introduces a methodology for the estimation of post-exercise heart recovery time and the heart’s adaptation to physical workload during free-living activities.

Video pulse rate variability analysis in stationary and motion conditions

BackgroundIn the last few years, some studies have measured heart rate (HR) or heart rate variability (HRV) parameters using a video camera. This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. To date, most of these works have obtained HRV parameters in stationary conditions, and there are practically no studies that obtain these parameters in motion scenarios and by conducting an in-depth statistical analysis.MethodsIn this study, a video pulse rate variability (PRV) analysis is conducted by measuring the pulse-to-pulse (PP) intervals in stationary and motion conditions. Firstly, given the importance of the sampling rate in a PRV analysis and the low frame rate of commercial cameras, we carried out an analysis of two models to evaluate their performance in the measurements. We propose a selective tracking method using the Viola–Jones and KLT algorithms, with the aim of carrying out a robust video PRV analysis in stationary and motion conditions. Data and results of the proposed method are contrasted with those reported in the state of the art.ResultsThe webcam achieved better results in the performance analysis of video cameras. In stationary conditions, high correlation values were obtained in PRV parameters with results above 0.9. The PP time series achieved an RMSE (mean ± standard deviation) of 19.45 ± 5.52 ms (1.70 ± 0.75 bpm). In the motion analysis, most of the PRV parameters also achieved good correlation results, but with lower values as regards stationary conditions. The PP time series presented an RMSE of 21.56 ± 6.41 ms (1.79 ± 0.63 bpm).ConclusionsThe statistical analysis showed good agreement between the reference system and the proposed method. In stationary conditions, the results of PRV parameters were improved by our method in comparison with data reported in related works. An overall comparative analysis of PRV parameters in motion conditions was more limited due to the lack of studies or studies containing insufficient data analysis. Based on the results, the proposed method could provide a low-cost, contactless and reliable alternative for measuring HR or PRV parameters in non-clinical environments.

Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement.

Remote camera-based measurement of physiology has great potential for healthcare and affective computing. Recent advances in computer vision and signal processing have enabled photoplethysmography (PPG) measurement using commercially available cameras. However, there remain challenges in recovering accurate noncontact PPG measurements in the presence of rigid head motion. When a subject is moving, their face may be turned away from one camera, be obscured by an object, or move out of the frame resulting in missing observations. As the calculation of pulse rate variability (PRV) requires analysis over a time window of several minutes, the effect of missing observations on such features is deleterious. We present an approach for fusing partial color-channel signals from an array of cameras that enable physiology measurements to be made from moving subjects, even if they leave the frame of one or more cameras, which would not otherwise be possible with only a single camera. We systematically test our method on subjects ( N=25) using a set of six, 5-min tasks (each repeated twice) involving different levels of head motion. This results in validation across 25 h of measurement. We evaluate pulse rate and PRV parameter estimation including statistical, geometric, and frequency-based measures. The median absolute error in pulse rate measurements was 0.57 beats-per-minute (BPM). In all but two tasks with the greatest motion, the median error was within 0.4 BPM of that from a contact PPG device. PRV estimates were significantly improved using our proposed approach compared to an alternative not designed to handle missing values and multiple camera signals; the error was reduced by over 50%. Without our proposed method, errors in pulse rate would be very high, and estimation of PRV parameters would not be feasible due to significant data loss.

Influence of acquisition frame-rate and video compression techniques on pulse-rate variability estimation from vPPG signal

In this paper, common time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmographic signal (vPPG) were compared with heart rate variability (HRV) parameters calculated from synchronized ECG signals. The dual focus of this study was to analyze the effect of different video acquisition frame-rates starting from 60 frames-per-second (fps) down to 7.5 fps and different video compression techniques using both lossless and lossy codecs on PRV parameters estimation. Video recordings were acquired through an off-the-shelf GigE Sony XCG-C30C camera on 60 young, healthy subjects (age 23±4 years) in the supine position. A fully automated, signal extraction method based on the Kanade-Lucas-Tomasi (KLT) algorithm for regions of interest (ROI) detection and tracking, in combination with a zero-phase principal component analysis (ZCA) signal separation technique was employed to convert the video frames sequence to a pulsatile signal. The frame-rate degradation was simulated on video recordings by directly sub-sampling the ROI tracking and signal extraction modules, to correctly mimic videos recorded at a lower speed. The compression of the videos was configured to avoid any frame rejection caused by codec quality leveling, FFV1 codec was used for lossless compression and H.264 with variable quality parameter as lossy codec. The results showed that a reduced frame-rate leads to inaccurate tracking of ROIs, increased time-jitter in the signals dynamics and local peak displacements, which degrades the performances in all the PRV parameters. The root mean square of successive differences (RMSSD) and the proportion of successive differences greater than 50 ms (PNN50) indexes in time-domain and the low frequency (LF) and high frequency (HF) power in frequency domain were the parameters which highly degraded with frame-rate reduction. Such a degradation can be partially mitigated by up-sampling the measured signal at a higher frequency (namely 60 Hz). Concerning the video compression, the results showed that compression techniques are suitable for the storage of vPPG recordings, although lossless or intra-frame compression are to be preferred over inter-frame compression methods. FFV1 performances are very close to the uncompressed (UNC) version with less than 45% disk size. H.264 showed a degradation of the PRV estimation directly correlated with the increase of the compression ratio.

Statin utilisation in a real-world setting: a retrospective analysis in relation to arterial and cardiovascular autonomic function.

Randomized trials suggest that statin treatment may lower blood pressure and influence cardiovascular autonomic function (CVAF), but the impact of duration of usage, discontinuation, and adherence to this therapy is unknown. We examined these issues with regard to blood pressure (BP)‐related variables in a large, population‐based study. Participants were 4942 adults (58% male; aged 50–84 years): 2179 on statin treatment and 2763 untreated. Days of utilization, adherence (proportion of days covered ≥0.8), and discontinuation (non‐use for ≥30 days immediately prior to BP measurement) of three statins (atorvastatin, pravastatin, and simvastatin) over a period of up to 2 years was monitored retrospectively from electronic databases. Systolic BP (SBP), diastolic BP (DBP), augmentation index, excess pressure, reservoir pressure, and CVAF (pulse rate and BP variability) parameters were calculated from aortic pressure waveforms derived from suprasystolic brachial measurement. Days of statin treatment had inverse relationships with pulse rate variability parameters in cardiac arrhythmic participants (20–25% lower than in statin non‐users) and with most arterial function parameters in everyone. For example, compared to untreated participants, those treated for ≥659 days had 3.0 mmHg lower aortic SBP (P < 0.01). Discontinuation was associated with higher brachial DBP and aortic DBP (for both, β = 2.0 mmHg, P = 0.008). Compared to non‐adherent statin users, adherent users had lower levels of brachial SBP, brachial DBP, aortic DBP, aortic SBP, and peak reservoir pressure (β = −1.4 to −2.6 mmHg). In conclusion, in a real‐world setting, statin‐therapy duration, non‐discontinuation and adherence associate inversely with BP variables and, in cardiac arrhythmias, CVAF parameters.

Relationships between heart-rate variability and pulse-rate variability obtained from video-PPG signal using ZCA

In this paper, classical time– and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmography signals (vPPG) were compared with heart rate variability (HRV) parameters extracted from ECG signals. The study focuses on the analysis of the changes observed during a rest-to-stand manoeuvre (a mild sympathetic stimulus) performed on 60 young, normal subjects (age: years). The objective is to evaluate if video-derived PRV indexes may replace HRV in the assessment of autonomic responses to external stimulation.Video recordings were performed with a GigE Sony XCG-C30C camera and analyzed offline to extract the vPPG signal. A new method based on zero-phase component analysis (ZCA) was employed in combination with a fully-automatic method for detection and tracking of region of interest (ROI) located on the forehead, the cheek and the nose.Results show an overall agreement between time and frequency domain indexes computed on HRV and PRV series. However, some differences exist between resting and standing conditions. During rest, all the indexes computed on HRV and PRV series were not statistically significantly different (p > 0.05), and showed high correlation (Pearson’s r > 0.90). The agreement decreases during standing, especially for the high-frequency, respiration-related parameters such as RMSSD (r = 0.75), pNN50 (r = 0.68) and HF power (r = 0.76). Finally, the power in the LF band (n.u.) was observed to increase significantly during standing by both HRV ( versus (n.u.); rest versus standing) and PRV ( versus (n.u.); rest versus standing) analysis, but such an increase was lower in PRV parameters than that observed by HRV indexes.These results provide evidence that some differences exist between variability indexes extracted from HRV and video-derived PRV, mainly in the HF band during standing. However, despite these differences video-derived PRV indexes were able to evince the autonomic responses expected by the sympathetic stimulation induced by the rest-to-stand manoeuvre.

Autonomic function, voice, and mood states

The present study examined the relationships among cardiovascular autonomic, acoustic, and mood states in resting young subjects. A total of 75 college students (men:women=53:22), aged 19-24years, were enrolled in this study. Each subject was asked to complete the profile of mood states and to produce a sustained vowel sound (/a/) for 3s to calculate acoustic parameters. We calculated pulse rate variability (PRV) parameters such as low frequency (LF), high frequency (HF), total power (TP) and the ratio of low frequency to high frequency power (LF/HF) by analyzing peak-to-peak intervals detected by photoplethysmography. Sympathovagal balance-related parameters such as LF/HF were dominant indicators of fatigue in men and women, whereas LF and HF were indicative of depression and anger in women. In terms of acoustic parameters, shimmer and standard deviation of the fundamental frequency (SD F (0)), which were related to roughness and instability in voice quality, were indicative of tension and depression in men and women. The results of this study suggest that mood states in resting young subjects can be estimated from PRV and acoustic parameters.

Effects of olanzapine and clozapine upon pulse rate variability.

Based upon their in vitro receptor binding profiles, the atypical antipsychotics clozapine and olanzapine exhibit cholinergic receptor binding of similar potency. Data comparing the in vivo anticholinergic effects, however, of these neuroleptics upon neurocardiac control are sparse. The goal of this study was to compare the in vivo effects of clozapine and olanzapine upon neurocardiac control by assessment of the pulse rate variability (PRV) in schizophrenic patients and healthy controls. Twenty patients with schizophrenia (according to DSM-III-R criteria) treated with either clozapine (100-600 mg/day) or olanzapine (10-20 mg/day), and ten healthy controls, were recruited into the study. PRV was assessed by continuously recording the skin blood volume in the fingertip of the second digit under resting conditions and PRV parameters were calculated. When significant differences in PRV parameters between the patients and controls were detected by Kruskal-Wallis tests, Mann-Whitney tests were used to test for group differences between the olanzapine- and clozapine-treated patients. In comparison to the healthy controls, the PRV parameters of the clozapine- and olanzapine-treated schizophrenic patients were significantly reduced. Indeed the reduction of PRV was significantly greater in the clozapine-treated group compared to the olanzapine-treated group (P<0.05). Compared to the controls, only the clozapine-treated patients showed a significantly diminished low-frequency (LF)/high frequency (HF)-ratio, a PRV parameter reflecting sympatho-vagal balance. The significantly greater reductions in PRV parameters of the clozapine-treated compared to olanzapine-treated patients may be caused by clozapine's higher affinity for alpha(1)-adrenergic receptors in vivo compared with olanzapine. The similar LF/HF ratios of the healthy controls and olanzapine-treated patients suggests that the sympathetic-parasympathetic modulation of PRV remains relatively unchanged even during olanzapine treatment.