Peter Charlton is a British Heart Foundation Research Fellow in the Department of Public Health and Primary Care, at the University of Cambridge. He specialises in the development of biomedical signal processing techniques to aid clinical decision making. He gained the degree of M.Eng. in Engineering Science in 2010 from the University of Oxford with first class honours. From 2010 to 2020, Peter conducted his research at King’s College London (KCL), developing techniques to continuously monitor respiratory and cardiovascular health using wearable sensors. His Ph.D. focused on using signal processing and machine learning techniques to identify acute deteriorations in hospital patients. In 2020, Peter was awarded a five-year fellowship to develop techniques to use clinical and consumer devices to enhance screening for atrial fibrillation. He works in collaboration with clinicians and industrial partners to translate his work into clinical practice.
Presently, Peter is developing methods to screen for atrial fibrillation (AF) by establishing criteria for analysing cardiovascular signals from clinical and consumer devices. He is developing techniques to robustly identify possible AF from wearable signals in daily life, in order to prompt screening. He is also developing techniques to streamline the screening process, and to assess the acceptability of wearables in screening.
During his doctoral research, Peter developed techniques to use wearable sensors to detect clinical deteriorations. He worked jointly with Guy’s and St Thomas’ NHS Trust to develop techniques to estimate respiratory rate, a key marker of deterioration, from wearable signals. Peter and his colleagues were awarded the 2017 Martin Black Prize for this work. During his Ph.D. he developed a novel technique for precise and unobtrusive respiratory rate monitoring in ambulatory patients. He used machine learning methods to combine the respiratory rates provided by this technique with additional parameters to continuously assess the likelihood of deterioration, and assessed this system’s clinical performance in a National Clinical Trial.
Peter’s postdoctoral research focused on assessing arterial stiffness, a predictor of cardiovascular events, from smart wearable signals. He developed a computational modelling approach to simulate arterial pulse wave signals under different cardiovascular conditions, facilitating research into how cardiovascular properties can be inferred from smart wearable signals. Peter was awarded the 2018 Best Early Career Researcher Award at the BioMedEng18 Conference for this work. He used this in silico modelling approach alongside clinical data analyses to develop novel techniques to assess arterial stiffness and vascular age.
Peter has a keen interest in making his research reproducible, and ensures that where possible the datasets and code he uses are made available for future use. Peter is an active member of the wider academic communities, serving on the International Advisory Board for the journal of Physiological Measurement, and the European Network for Research in Vascular Ageing.
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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