Conference Paper

Smartphone PPG: signal processing, quality assessment, and impact on HRV parameters

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Abstract

Photoplethysmography (PPG) is a simple optical technique used to detect blood volume changes in the micro-vascular bed of tissue in order to track the heartbeat. Smart-phone PPG, performed with the phones camera, has became popular in recent years due to a boom in digital health apps that help people monitor their health parameters. However, many apps struggle with getting readings that are accurate enough to estimate heart rate variability (HRV) one of the most popular biomarkers in the preventive health space. The main obstacle is the multitude of factors that impact PPG results: unique technical characteristics of different smartphone models, frames per second (FPS) rate and the way color is recoarded, brightness and ambient flash levels, finger placement, in-measurement movement, etc. These factors may decrease the accuracy of the signal extracted from the camera's video stream and produce additional errors in the computation of HRV parameters. Thus, there is a need to estimate signal quality and predict possible bias in HRV parameter calculation. In this paper, we describe the method for processing signal from smartphone cameras, estimating signal quality, recognizing RR intervals, and predicting bias of simple HRV parameters.

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... PPG tracks blood volume changes in peripheral blood vessels by illuminating the skin and measuring changes in light absorption. In practice, PPG signals are often collected using wrist-worn devices with optical sensors, such as smartwatches [7,8], or using a smartphone camera attached to a user's finger [9,10] (we will refer to such signals as smartphone PPG). PPG signals are used to estimate heart rate [7,8,10,11], as well as blood oxygen saturation, blood pressure [6,10], etc. ...
... In our algorithm, we propose to analyze the signal during the detected intervals to estimate their reliability. A real-time algorithm for peak detection and signal quality estimation in smartphone PPG was proposed in [9]. As a real-time algorithm, it was restricted in computational complexity and the variety of methods that can be used. ...
Article
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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: TROIKA and PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
... A. Data acquisition RR-interval sequences from 1,471 subjects were obtained through the Welltory app, which subjects used to take heart rate variability measurements with their smartphone cameras or Apple Watch while in a resting state, after completing a stress self-assessment questionnaire (Perceived Stress Questionnaire (PSQ)) on the same day. We excluded measurements that showed possible arrhythmias in research subjects [23] , as well as low-quality measurements [24] , because these factors can significantly distort HRV metrics. The RR-interval sequences were used to calculate AMo, pNN50, and MedSD values. ...
... 1. Participants filled out the Perceived Stress Questionnaire after October 1, 2020. 2. Participants took a heart rate variability measurement after completing the Perceived Stress Questionnaire. 3. Participants were between the ages of 25 and 60. 4. The quality of the heart rate variability measurements taken by participants was high enough (measurement quality is calculated based on data obtained from the measurement device [24] ). ...
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Background: Multiple 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. Objective: The 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. Setting: We 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. Subjects: To 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). Measurements: We 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]. Results: Physiological 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. Conclusions: Physiological 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.
... Since tissues reflect light more than the hemoglobin found in red blood cells [25], measuring the amount of light that returns to the device can give an idea of how much blood is in the site. These devices are so widespread now that they have been integrated into many smartphones [26] and smart watches [27] created in the last decade. Although BVP signals have successfully been used to measure peoples' heart rates in recent years [28], these signals can provide a lot more information than just that. ...
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Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we subdivided our findings in terms of two different emotional categories, namely—discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the individual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring.
... Table 8 and 9 provide respectively a summary of the features used to design SQIs for imaging PPG and of the existing methods. Relative difference between highest and second highest amplitude of the signal spectrum [101] Maximum scalar product between PPG periodogram [103] and predefined filters [104] Probabilistic SNR (obtained using HMM models) [61] HMM = Hidden Markov Model. iPPG and cPPG share common properties such as semi-periodicity and the extractable features of their respective pulses. ...
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Photoplethysmography is a method to visualize the variation in blood volume within tissues with light. The signal obtained has been used for the monitoring of patients, interpretation for diagnosis or for extracting other physiological variables (e.g., pulse rate and blood oxygen saturation). However, the photoplethysmography signal can be perturbed by external and physiological factors. Implementing methods to evaluate the quality of the signal allows one to avoid misinterpretation while maintaining the performance of its applications. This paper provides an overview on signal quality index algorithms applied to photoplethysmography. We try to provide a clear view on the role of a quality index and its design. Then, we discuss the challenges arising in the quality assessment of imaging photoplethysmography.
... In this study, we used interbeat interval data extracted from the Welltory app. These data were collected through photoplethysmography (PPG) technology with smartphone cameras (Tyapochkin et al., 2019), wrist-worn smartwatches, and wrist-worn bands synchronized with the Welltory app. Age, sex, weight, and height were also extracted from the Welltory app. ...
Preprint
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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.
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Non-linear heart rate (HR) dynamics characterizes the fractal properties and complexity of the variations in HR. Ventricular and supraventricular ectopic beats might introduce a mathematical artefact to the analyses on sinus rhythm. We therefore evaluated the effects of different editing practices for ectopic beats such that 753 40-min ECG recordings were (i) not edited for the ectopic beats, or the ectopic beats were edited with (ii) an interpolation or with (iii) a deletion method before the analyses of non-linear HR dynamics. The non-linear HR dynamics analyses included detrended fluctuation analysis (DFA), approximate entropy, symbolic dynamics (SymDyn), fractal dimension and return map (RM). We found that the short-term scaling exponent (alpha1) of DFA, forbidden words of SymDyn and RM were sensitive measurements to the ectopic beats and there were strong correlations between these measurements and the number of ectopic beats. In addition, the unedited ectopic beats significantly lowered the stability of these measurements. However, the editing either with interpolation or deletion method corrected the measurements for the bias caused by the ectopic beats. On the contrary, the entropy measurements were not as sensitive to the ectopic beats. In conclusion, the ectopic beats affect the non-linear HR dynamics of sinus rhythm differently, causing a more marked bias in fractal than in complexity measurements of non-linear HR dynamics. This erroneous effect of ectopic beats can be corrected with a proper editing of these measurements. Therefore, there is an obvious need for standardized editing practices for ectopic beats before the analysis of non-linear HR dynamics.
The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections.
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