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Photoplethysmography is now widely utilised by clinical devices such as pulse oximeters, and wearable devices such as smartwatches. It holds great promise for health monitoring in daily life. This editorial considers whether it would be possible and beneficial to establish best practices for photoplethysmography signal acquisition and processing. It reports progress made towards this, balanced with the challenges of working with a diverse range of photoplethysmography device designs and intended applications, each of which could benefit from different approaches to signal acquisition and processing. It concludes that there are several potential benefits to establishing best practices. However, it is not yet clear whether it is possible to establish best practices which hold across the range of photoplethysmography device designs and applications., This paper is based upon work from COST ACTION CA18216 ‘Network for Research in Vascular Ageing’, supported by COST (European Cooperation in Science and Technology): www.cost.eu . The work was supported by British Heart Foundation (BHF) grant FS/20/20/34626, and by the Estonian Ministry of Education and Research under personal post-doctoral research funding PUTJD815.

Presentation Title Wearable devices for health monitoring Abstract In the future, wearable photoplethysmography devices could be used to monitor the population's health unobtrusively, helping prevent, identify and treat disease. Potential clinical applications for such devices have emerged in only ten years since they entered the consumer market, including detecting atrial fibrillation. Whilst the developments to date are a great achievement, there is potential for wearable photoplethysmography devices to be used for many more clinical applications. In this talk, Peter Charlton will firstly discuss potential future applications such as blood pressure monitoring, sleep apnea detection, and infectious disease surveillance. Second, he will outline key steps required to make these applications a reality: obtaining high quality photoplethysmogram (PPG) signals; reliably extracting physiological information from PPG signals; and using such information wisely to inform clinical decision making. Third, he will discuss challenges in the development of wearable photoplethysmography devices for clinical applications, and potential solutions. It is hoped that this vision will prompt dialogue about how to develop wearable photoplethysmography devices to be of the greatest possible benefit to society., Presented at the Huawei Future Device Technology Summit Program, Helsinki, on 8th November 2022.

Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

This talk covers the fundamentals of photoplethysmography, highlights promising clinical applications, and suggests areas for future research. A video of the talk is available here: https://www.youtube.com/watch?v=qnN39k26ymw Originally presented at the 2022 IEEE EMBC Conference., This work was supported by British Heart Foundation (BHF) grant [FS/20/20/34626].

An overview of the most promising opportunities to use photoplethysmography in public health. The talk introduces photoplethysmography, covering the physiological and technical fundamentals. It then discusses potential clinical applications, drawing on real-life examples of how they could be used to improve health outcomes. Finally, it discusses pressing directions for future research to ensure photoplethysmography can be used to inform clinical decisions safely and robustly. A video of this presentation is available at: https://youtu.be/1fQ16Cdxxto Presented at 'The Conference on the Future of Europe - Prescribing a healthier future for Europeans' on 25 February 2022., Funded by the British Heart Foundation Grant FS/20/20/34626

An introduction to photoplethysmography, in a minute (and a bit), covering: use in clinical devices such as pulse oximeters use in wearables such as smartwatches and fitness trackers potential applications, such as detecting atrial fibrillation, assessing cardiovascular health, and monitoring breathing. Accompanying video available at:https://youtu.be/HnXDvN4WNX8

Objective: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. Approach: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. Main results: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of

The photoplethysmogram (PPG) signal is an attractive candidate for unobtrusive blood pressure (BP) monitoring, as it is widely measured by wrist-worn devices. However, most studies of PPG-based BP estimation techniques have used finger PPG signals. This study compares PPG-based BP estimation when using finger and wrist optical sensors. Subject-specific linear regression models employing pulse transit time, PPG intensity ratio and heart rate as features for BP estimation were trained and tested using PPGs and reference continuous BP values obtained from 22 healthy participants performing 2 cold pressor tests. Mean±stan - dard deviation of differences, and the mean absolute difference between reference and estimated systolic BP values were: 0.47±10.44 mmHg and 7.78 mmHg for finger PPG signals; and 1.05±12.86 mmHg and 9.69 mmHg for wrist PPG signals. Increases and decreases in systolic BP of at least 10 mmHg were detected with F1 scores of: 0.81 and 0.76 for finger PPG; and 0.75 and 0.61 for wrist PPG. Models performed better with finger PPG signals. Different signal processing approaches were required for finger and wrist signals, indicating that finger-based BP estimation models should not be generalized directly to wrist PPGs.

This document contains tables,figures and a mathematical derivation that are auxiliary to the main content of thearticleNovel pressure wave separation analysis for cardiovascular function assessment highlights major role of aortic rootwhich is currently under review.

This presentation provides an introduction to the'Respiratory rate algorithms for wearables' project. It was originally presented at a meeting at the University of Cambridge on 13th May 2021. The accompanying video is available at:https://www.youtube.com/watch?v=C3JPImVkouc Further information on the project is available at:http://peterhcharlton.github.io/RRest/

A presentation given on 18th March 2021 as part of the Webinar series on STEM in Healthcare, organised by City, University of London, and IPEM (the Institute of Physics and Engineering in Medicine).

Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data.