Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP.
Ontology highlight
ABSTRACT:
SUBMITTER: Bellenger CR
PROVIDER: S-EPMC8160717 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as - 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA α1 (8.25%) and DFA α2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable.ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registered.
| S-EPMC9852147 | biostudies-literature
Project description:Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.
| S-EPMC10689737 | biostudies-literature
Project description:BACKGROUND:The goal of this study was to determine a set of timing, shape, and statistical features available through noninvasive monitoring of maternal electrocardiogram and photoplethysmography that identifies preeclamptic patients. METHODS:Pregnant women admitted to Labor and Delivery were monitored with pulse oximetry and electrocardiogram for 30 minutes. Photoplethysmogram features and heart rate variability were extracted from each data set and applied to a sequential feature selection algorithm to discriminate women with preeclampsia with severe features, from normotensive and hypertensive controls. The classification boundary was chosen to minimize the expected misclassification cost. The prior probabilities of the misclassification costs were assumed to be equal. RESULTS:Thirty-seven patients with clinically diagnosed preeclampsia with severe features were compared with 43 normotensive controls; all were in early labor or beginning induction. Six variables were used in the final model. The area under the receiver operating characteristic curve was 0.907 (standard error [SE] = 0.004) (sensitivity 78.2% [SE = 0.3%], specificity 89.9% [SE = 0.1%]) with a positive predictive value of 0.883 (SE = 0.001). Twenty-eight subjects with chronic or gestational hypertension were compared with the same preeclampsia group, generating a model with 5 features with an area under the curve of 0.795 (SE = 0.007; sensitivity 79.0% [SE = 0.2%], specificity 68.7% [SE = 0.4%]), and a positive predictive value of 0.799 (SE = 0.002). CONCLUSIONS:Vascular parameters, as assessed noninvasively by photoplethysmography and heart rate variability, may have a role in screening women suspected of having preeclampsia, particularly in areas with limited resources.
| S-EPMC5820156 | biostudies-literature
Project description: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.
| S-EPMC8401087 | biostudies-literature
Project description:The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R-R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis.
| S-EPMC9028181 | biostudies-literature
Project description:Optical heart rate monitoring (OHR) with reflective wrist photoplethysmography is a technique mainly used in the wellness application domain for monitoring heart rate levels during exercise. In the absence of motion, OHR technique is also able to estimate individual beat-to-beat intervals relatively well and can therefore also be used, for example, in monitoring of cardiac arrhythmias, stress, or sleep quality through heart rate variability (HRV) analysis. HRV analysis has also potential in monitoring the recovery of patients, e.g. after a medical intervention. However, in order to detect subtle changes, the calculated HRV parameters should be sufficiently accurate and very few studies exist that asses the accuracy of OHR derived HRV in non-healthy subjects. In this paper, we present a method to estimate beat-to-beat-intervals (BBIs) from reflective wrist PPG signal and evaluated the accuracy of the proposed method in estimating BBIs in a cross-sectional study with 29 hospitalized patients (mean age 70.6 years) in 24-h recordings performed after peripheral vascular surgery or endovascular interventions. Finally, we evaluate the accuracy of more than 30 commonly used HRV parameters and find that the accuracy of certain metrics, for example SDNN and triangular index, shown in the literature to be associated with the deterioration of the status of the patients during recovery from surgical intervention, could be adequate for patient monitoring. On the other hand, the parameters more affected by the high-frequency content of the HRV and especially the LF/HF-ratio should be used with caution.
| S-EPMC8047026 | biostudies-literature
Project description:Physical fatigue can be assessed using heart rate variability (HRV). We measured HRV at rest and in a fatigued state using impulse-radio ultra wideband (IR-UWB) radar in a noncontact fashion and compared the measurements with those obtained using electrocardiography (ECG) to assess the reliability and validity of the radar measurements. HRV was measured in 15 subjects using radar and ECG simultaneously before (rest for 10 min before exercise) and after a 20-min exercise session (fatigue level 1 for 0–9 min; fatigue level 2 for 10–19 min; recovery for ≥ 20 min after exercise). HRV was analysed in the frequency domain, including the low-frequency component (LF), high-frequency component (HF) and LF/HF ratio. The LF/HF ratio measured using radar highly agreed with that measured using ECG during rest (ICC = 0.807), fatigue-1 (ICC = 0.712), fatigue-2 (ICC = 0.741) and recovery (ICC = 0.764) in analyses using intraclass correlation coefficients (ICCs). The change pattern in the LH/HF ratios during the experiment was similar between radar and ECG. The subject’s body fat percentage was linearly associated with the time to recovery from physical fatigue (R2 = 0.96, p < 0.001). Our results demonstrated that fatigue and rest states can be distinguished accurately based on HRV measurements using IR-UWB radar in a noncontact fashion.
| S-EPMC9392064 | biostudies-literature
Project description:In France, new cancer cases keep on increasing with around 150 000 deaths yearly. Cancer therapy research is constantly evolving. Indeed, several studies explore new treatments or their combination with conventional cancer treatments. But, at the same time, complementary and alternative medicines, as osteopathy, remain little explored upon their role in the combination with conventional therapy.
Several studies showed indirect interaction between vagus nerve and cancer. Firstly, vagus nerve regulates homeostasis and immunity by reducing systemic inflammation while maintaining local inflammation and antitumor effects. Secondly, vagus nerve stimulation increases Heart Rate Variability (HRV). Moreover, a higher HRV is associated with an improvement of vital prognosis in cancer patients. Vagus nerve could be stimulated by noninvasive osteopathic manipulations.
This prospective, monocentric and randomized study is a collaboration between the Centre Hospitalier d’Avignon and the Institut de Formation en Ostéopathie du Grand Avignon. It focuses on using noninvasive osteopathic mobilizations to stimulate vagus nerve. Indeed, this study aims to evaluate effects of vagus nerve osteopathic stimulations on HRV in patients with lung cancer, colorectal cancer, Non Hodgkin Lymphoma or Multiple Myeloma. More specifically, this study will tell us whether vagus nerve noninvasive osteopathic stimulations induce increase of HRV associated with a decrease of systemic inflammation and an improvement of patient’s quality of life.
| 2364606 | ecrin-mdr-crc
Project description: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.
| S-EPMC7390908 | biostudies-literature
Project description:Heart rate variability (HRV), using electrocardiography (ECG), has gained popularity as a biomarker of the stress response. Alternatives to HRV monitoring, like photoplethysmography (PPG), are being explored as cheaper and unobtrusive non-invasive technologies. We report a new wireless PPG sensor that was tested in detecting changes in HRV, elicited by a mentally stressful task, and to determine if its signal can be used as a surrogate of ECG for HRV analysis. Data were collected simultaneously from volunteers using a PPG and ECG sensor, during a resting and a mentally stressful task. HRV metrics were extracted from these signals and compared to determine the agreement between them and to determine if any changes occurred in the metrics due to the stressful task. For both tasks, a moderate/good agreement was found in the mean interbeat intervals, SDNN, LF, and SD2, and a poor agreement for the pNN50, RMSSD|SD1, and HF metrics. The majority of the tested HRV metrics obtained from the PPG signal showed a significant decrease caused by the mental task. The disagreement found between specific HRV features imposes caution when comparing metrics from different technologies. Nevertheless, the tested sensor was successful at detecting changes in the HRV caused by a mental stressor.
| S-EPMC7411830 | biostudies-literature