The 2023 wearable photoplethysmography roadmap

Charlton, Peter; Allen, John; Bailón, Raquel; Baker, Stephanie; Behar, Joachim A.; Chen, Fei; Clifford, Gari D.; Clifton, David A.; Davies, Harry J.; Ding, Cheng; Ding, Xiaorong; Dunn, Jessilyn; Elgendi, Mohamed; Ferdoushi, Munia; Franklin, Daniel; Gil, Eduardo; Hassan, Md. Farhad; Hernesniemi, Jussi; Hu, Xiao; Ji, Nan; Khan, Yasser; Kontaxis, Spyridon; Korhonen, Ilkka; Kyriacou, Panayiotis A.; Laguna, Pablo; Lázaro, Jesús; Lee, Chung-Keun; Levy, Jeremy; Li, Yumin; Liu, Chengyu; Liu, Chun‐Feng; Lü, Lei; Mandic, Danilo P.; Marozas, Vaidotas; Mejía-Mejía, Elisa; Mukkamala, Ramakrishna; Nitzan, Meir; Pereira, Tânia; Poon, Chi‐Sang; Ramella‐Roman, Jessica C.; Saarinen, Harri; Shandhi, Md. Mobashir Hasan; Shin, Hyunki; Stansby, Gerard; Tamura, Toshiyo; Vehkaoja, A; Wang, Will Ke; Zhang, Yuan-Ting; Zhao, Ni; Zheng, Dingchang; Zhu, Tingting

doi:10.1088/1361-6579/acead2

Cited by 7 publications

(4 citation statements)

References 314 publications

(491 reference statements)

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“…We propose a nuanced perspective on PPG signal quality assessment rather than adhering to the dichotomous approach of designating signals as merely black or white (Charlton et al 2023). Instead, we suggest the computation of a signal quality index (SQI) as a continuous metric (Guo et al 2021b).…”

Section: Ppg Signal Qualitymentioning

confidence: 99%

“…To this end, photoplethysmography (PPG) has emerged as a preferred technology, with a ubiquitous adoption in over 71% of wearable devices given its capacity to capture heart rhythm dynamics (Charlton et al 2023). The physiological foundation of PPG for AF detection lies in the fact that irregular heartbeats induce variations in cardiac output, leading to fluctuations in peripheral blood volume.…”

Section: Introductionmentioning

confidence: 99%

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Photoplethysmography based atrial fibrillation detection: a continually growing field

Ding,

Xiao,

Wang

et al. 2024

Physiol. Meas.

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Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.

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Section: Ppg Signal Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Photoplethysmography based atrial fibrillation detection: a continually growing field

Ding,

Xiao,

Wang

et al. 2024

Physiol. Meas.

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“…pulse transit time (PTT) or pulse arrival time (PAT), and pulse wave analysis (PWA) (Mukkamala et al 2015(Mukkamala et al , 2021 became very popular, especially in the context of cuffless BP measurement and dedicated health interventions. There has been much research on the relationship of PAT/PTT and BP changes published in recent years (Martínez et al 2018, Liang et al 2019, Block et al 2020, Frey et al 2022, Pilz et al 2022 with a pathway for upcoming solutions (Charlton et al 2022(Charlton et al , 2023, whereby different approaches and technologies have been used. The use of pulse wave parameters may add additional information beyond PAT/PTT as pressure changes do not only affect arrival but as well the shape and the timings of the pressure wave (Natarajan et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention

Hametner,

Maurer,

Sehnert

et al. 2024

Physiol. Meas.

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Background: Non-invasive continuous blood pressure monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic blood pressure (change), became very popular recently, especially in the context of cuffless blood pressure measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.Objective: The aim of this work is therefore the investigation whether PAT follows oscillometric systolic blood pressure readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.Methods: A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric blood pressure and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between blood pressure and PAT. Results: The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in blood pressure, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic blood pressure increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic blood pressure at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic blood pressure and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01], p<0.048), and from physical to mental task (-0.51 [-0.77, -0.14], p=0.001), but not for baseline to mental task (-0.12 [-0,43,0,20], p=0.50) in the experimental group.Conclusion: PAT and the used digital, handheld device proved to register changes in blood pressure and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support blood pressure management by tracking relative, not absolute, blood pressure changes during non-pharmacological interventions.

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“…This is explored through algorithms like PCA (Lewandowska et al, 2011), CHROM-based rPPG 10.3389/fphys.2023.1296277 (De Haan and Jeanne, 2013), LGI (Pilz et al, 2018), POS (Wang et al, 2016), and emerging deep learning methods (Yu et al, 2019;Schrumpf et al, 2021), over diverse datasets (Haugg et al, 2022;van Es et al, 2023). Understanding the reliability of RGB signals, influenced by recording settings and subject factors, is crucial when compared to cPPG for robust signal transformation (Charlton et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG

Ontiveros,

Elgendi,

Missale

et al. 2023

Front. Physiol.

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Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson’s correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment.

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The 2023 wearable photoplethysmography roadmap

Cited by 7 publications

References 314 publications

Photoplethysmography based atrial fibrillation detection: a continually growing field

Photoplethysmography based atrial fibrillation detection: a continually growing field

Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG

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