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Cambridge Cardiovascular



Using clinical and consumer devices to enhance screening for atrial fibrillation

In his present research, Peter is developing methods to screen for atrial fibrillation by establishing criteria for analysing cardiovascular signals from clinical and consumer devices. Atrial fibrillation (AF) is associated with a fivefold increase in stroke risk, and yet is undiagnosed in 425,000 people in England; screening could be enhanced through rhythm monitoring in daily life, although criteria are yet to be established for targeted screening. In this project, running from 2020-2025, Peter is analysing cardiovascular signals acquired in AF screening studies to develop evidence for targeted AF screening using clinical and consumer devices. His aims are:

  1. To establish criteria for detecting possible AF from everyday wearables.
  2. To establish criteria to identifying patients who are unlikely to exhibit AF during home rhythm monitoring, and therefore do not require AF screening.
  3. To assess the acceptability and performance of clinical and consumer devices for home rhythm monitoring.

The criteria for use in AF screening, and the accompanying datasets and algorithms, will be made available to enhance screening and support future research into using wearable data to inform anticoagulation decisions.

For further information on Peter’s research please see:


Key publications: 


[1]         Charlton, P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P., & Alastruey, J., “Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes,” AJP: Heart and Circulatory Physiology, vol. 317, no. 5, pp.H1062-H1085, 2019. CrossRef Additional Information

[2]         Charlton, P.H., Celka, P., Farukh, B., Chowienczyk, P., & Alastruey, J., “Assessing Mental Stress from the Photoplethysmogram: A Numerical Study,” Physiological Measurement, vol. 39, no. 5, p. 054001, 2018. CrossRef

[3]         Charlton, P.H., Birrenkott, D., Bonnici, T., Pimentel, M., Johnson, A. E. W., Alastruey, J., Tarassenko L., Watkinson, P.J., Beale, R., & Clifton D., “Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram: A Review,” IEEE Reviews in Biomedical Engineering, vol. 11, pp.2-18, 2018. CrossRef  Additional Information

[4]         Charlton, P.H., Bonnici T., Tarassenko L., Alastruey, J., Clifton D., Beale R., & Watkinson P.J., “Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants,” Physiological Measurement, vol. 38, pp.669-690, 2017. CrossRef  Additional Information

[5]         Charlton, P.H. and Bonnici T., Tarassenko L., Clifton D., Beale R., & Watkinson P.J., “An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram,”Physiological Measurement, vol. 37, no. 4, pp.610-626, 2016. CrossRef  Additional Information

[6]         Vennin, S., Li, Y., Willemet, M., Fok, H., Gu, H., Charlton, P., Alastruey, J. & Chowienczyk, P., “Identifying hemodynamic determinants of central pulse pressure: a combined numerical and physiological approach,” Hypertension, vol. 70, no. 6, pp. 1176-1182, 2017. CrossRef

[7]         Pimentel, M.A.F., Johnson, A.E.W., Charlton, P.H., Birrenkott D., Watkinson, P.J., Tarassenko, L., & Clifton, D.A., “Towards a robust estimation of respiratory rate from pulse oximeters,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1914-1923, 2017. CrossRef  BIDMC Dataset

[8]         Aboab, J., Celi, L., Charlton, P., Feng, M., Ghassemi, M., Marshall, D., Mayaud, L., Naumann, T., McCague, N., Paik, K., Pollard, T., Resche-Rigon, M., Salciccioli, J., & Stone, D., “A ‘datathon’ model to support cross-disciplinary collaboration” Science Translational Medicine, vol. 8, no. 333, pp. 333ps8, 2016. CrossRef

[9]         Orphanidou, C., Bonnici, T., Charlton, P.H., Clifton, D., Vallance, D., & Tarassenko, L., “Signal quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no.3, pp.832–838, 2015.CrossRef

[10]      Meredith, D.J., Clifton, D., Charlton, P., Brooks, J., Pugh, C.W., & Tarassenko, L., “Photoplethysmographic derivation of respiratory rate: a review of relevant physiology,” Journal of Medical Engineering & Technology, vol. 36, no. 1, pp. 1-7, 2012. CrossRef

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Person keywords: 
physiological measurement
atrial fibrillation
digital health
machine learning