Objective: Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content.
Methods: PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests.
Results: From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz.
Conclusion: The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application.
Significance: The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.
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http://dx.doi.org/10.1016/j.cmpb.2022.106724 | DOI Listing |
Sci Rep
January 2025
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals.
View Article and Find Full Text PDFJ Med Eng Technol
December 2024
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample.
View Article and Find Full Text PDFJ Vasc Access
December 2024
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan.
Introduction: Vascular access (VA) is essential for patients with hemodialysis, and its dysfunction is a major complication that can reduce quality of life or even threaten life. VA patency is not only difficult to predict on an individual basis, but also challenging to predict in real-time. To overcome this challenge, this study aimed to develop a machine learning approach to predict 6-month primary patency (PP) using photoplethysmography (PPG) signals acquired from the tips of both index fingers.
View Article and Find Full Text PDFIn the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning.
View Article and Find Full Text PDFPhysiol Meas
December 2024
University of Glasgow James Watt School of Engineering, James Watt School of Engineering, Glasgow, Glasgow, G12 8QQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Objective: We study the changes in morphology of the photoplethysmography (PPG) signals-acquired from a select group of South Asian origin-through a low-cost PPG sensor, and correlate it with healthy aging which allows us to reliably estimate the vascular age and chronological age of a healthy person as well as the age group he/she belongs to.
Methods: Raw infrared PPG data is collected from the finger-tip of 173 appar- ently healthy subjects, aged 3-61 years, via a non-invasive low- cost MAX30102 PPG sensor. In addition, the following metadata is recorded for each subject: age, gender, height, weight, family history of cardiac disease, smoking history, vitals (heart rate and SpO2).
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