Heart rate variability (HRV) is the fluctuation in the time interval between consecutive heartbeats, the measurement of which is a non-invasive method for assessing the autonomic status. The autonomic nervous system plays an important role in physiological situations, and in various pathological processes such as in cardiovascular diseases and viral infections. This study examined the cardiac autonomic responses, as measured by HRV before, after, and during coronavirus disease. In this study, we used beat interval data extracted from the Welltory app from 14 eligible subjects (9 men and 5 women) with a mean age (SD) of 44 (8.7) years. HRV analysis was performed through an assessment of time-domain indices (SDNN and RMSSD). Group analysis did not reveal any statistical difference between HRV metrics before, during, and after COVID-19. However, HRV at the individual level showed a statistically significant individual change during COVID-19 in some users. These data further support the usefulness of using individual-level HRV tracking for the detection of early diseases inclusive of COVID-19.
Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person’s movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals’ self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKAand PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
BackgroundMultiple studies have shown that the state of stress has a negative impact on decision-making, the cardiovascular system, and the autonomic nervous system [1]. In light of this, we have developed a mobile application in order to assess user stress levels based on the state of their physiological systems. This assessment is based on heart rate variability [2], [3], [4], [5], which many wearable devices such as Apple Watch have learned to measure in the background. We developed a proprietary algorithm that assesses stress levels based on heart rate variability analysis, and this research paper shows that assessments positively correlate with subjective feelings of stress experienced by users.ObjectiveThe objective of this paper is to study the relationship between HRV-based physiological stress responses and Perceived Stress Questionnaire self-assessments in order to validate Welltory measurements as a tool that can be used for daily stress measurements.SettingWe analyzed data from Welltory app users, which is publicly available and free of charge. The app allows users to complete the Perceived Stress Questionnaire and take heart rate variability measurements, either with Apple Watch or with their smartphone cameras.SubjectsTo conduct our study, we collected all questionnaire results from users between the ages of 25 and 60 who also took a heart rate variability measurement on the same day, after filling out the Questionnaire. In total, this research paper includes results from 1,471 participants (602 men and 869 women).MeasurementsWe quantitatively measured physiological stress based on AMo, pNN50, and MedSD values, which were calculated based on sequences of RR-intervals recorded with the Welltory app. We assessed psychological stress levels based on the Perceived Stress Questionnaire (PSQ) [6], [7].ResultsPhysiological stress reliably correlates with self-assessed psychological stress levels - low for subjects with low psychological stress levels, medium for subjects with medium psychological stress levels, and high for subjects with high psychological stress levels. On a scale of 0-100%, median physiological stress is 48.7 (95% CI of 45.2-50.7%), 56.4 (95% CI of 54.3-58.9), and 62.5 (95% CI of 59.7-66.3) for these groups, respectively.ConclusionsPhysiological stress response, which is calculated based on heart rate variability analysis, on average increases as psychological stress increases. Our results show that HRV measurements significantly correlate with perceived psychological stress, and can therefore be used as a stress assessment tool.
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