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.
Peter 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 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.
During his research Peter has studied a range of sensing technologies (e.g. photoplethysmography and electrocardiography) and investigated their potential utility in different clinical settings (from intensive care monitoring to wearable monitoring in daily life).
Presently, Peter is developing methods to use clinical and consumer devices to screen for atrial fibrillation (AF). He is developing techniques to analyse two key physiological signals to robustly identify AF in daily life: photoplethysmography and electrocardiogram signals.
Photoplethysmography is an optical sensing technique, which is widely used in smartwatches and fitness trackers for heart rate monitoring. Peter has investigated how a range of physiological parameters could be obtained from photoplethysmogram (PPG) signals, including respiratory rate, and arterial stiffness. He is now investigating how best to use wearable PPG signals to assess heart rhythm and reliably detect episodes of AF in daily life.
For more information on photoplethysmography, see:
• This review paper, which summarises the fundamentals of photoplethysmography, its use in wearables, signal processing, and its potential clinical applications.
• This short video, which provides an introduction to photoplethysmography in a minute (and a bit).
The electrocardiogram (ECG) is used to diagnose a range of heart conditions. Whilst electrocardiography measurements used to be confined to clinical settings, consumer devices such as smart watches can now measure a single-lead ECG on demand. New applications for single-lead ECGs are emerging, such as using them to detect atrial fibrillation (AF) at a population level.
Peter is working with the SAFER team to investigate how best to use single-lead ECGs (such as those recorded from handheld and wrist-worn devices) to detect AF. Work to date on streamlining the use of these ECG signals in AF screening is available here and here.
Peter is currently leading the SAFER Wearables Study, which will assess the acceptability and performance of wearables for atrial fibrillation screening in older adults. The study will focus on the technologies used in clinical devices such as chest-patches, and wrist-worn consumer devices such as fitness trackers.
During his doctoral research, Peter developed techniques to use wearable sensors to detect clinical deteriorations in hospital patients. By combining signal processing and machine learning, he developed approaches to continuously monitor a patient’s likelihood of deterioration, and assessed their clinical performance.
Peter and his colleagues were awarded the 2017 Martin Black Prize for this work.
During his Ph.D., Peter worked jointly with Guy’s and St Thomas’ NHS Trust to develop techniques to estimate respiratory rate from wearable signals. He developed a novel technique for precise and unobtrusive respiratory rate monitoring in ambulatory patients.
Key papers on the work include:
– 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
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.
Key papers on the work include:
– 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
Peter continues this work alongside colleagues in VascAgeNet – the European Network for Research in Vascular Ageing.