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Cambridge Public Health

 
British Heart Foundation Research Fellow

Peter Charlton is a British Heart Foundation Research Fellow in the Department of Public Health and Primary Care, at the University of Cambridge. He develops biomedical signal processing techniques to analyse data from digital wearable devices for 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, developing techniques to use wearables to monitor cardiovascular and respiratory health. His Ph.D. focused on using signal processing and machine learning techniques to identify acute deteriorations in hospital patients. Peter is currently developing techniques to use clinical and consumer devices in screening for atrial fibrillation, as part of the SAFER programme. He is leading a clinical study to assess the acceptability and performance of wearables in older adults.

Peter works in collaboration with clinicians and industrial partners to translate his work into clinical practice.

Publications from Elements

Journal articles

2024 (Accepted for publication)

  • Modi, R., 2024 (Accepted for publication). The feasibility of population screening for paroxysmal atrial fibrillation using handheld ECGs Europace,
  • 2024

  • Goda, MÁ., Charlton, PH. and Behar, JA., 2024. pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis. Physiol Meas, v. 45
    Doi: http://doi.org/10.1088/1361-6579/ad33a2
  • Zanelli, S., Agnoletti, D., Alastruey, J., Allen, J., Bianchini, E., Bikia, V., Boutouyrie, P., Bruno, RM., Climie, R., Djamaleddine, D., Gkaliagkousi, E., Giudici, A., Gopcevic, K., Grillo, A., Guala, A., Hametner, B., Joseph, J., Karimpour, P., Kodithuwakku, V., Kyriacou, PA., Lazaridis, A., Lonnebakken, MT., Martina, MR., Mayer, CC., Nabeel, PM., Navickas, P., Nemcsik, J., Orter, S., Park, C., Pereira, T., Pucci, G., Amado Rey, AB., Salvi, P., Gonçalves Seabra, AC., Seeland, U., van Sloten, T., Spronck, B., Stansby, G., Steens, I., Stieglitz, T., Tan, I., Veerasingam, D., Wassertheurer, S., Weber, T., Westerhof, BE. and Charlton, PH., 2024. Developing technologies to assess vascular ageing: a roadmap from VascAgeNet. Physiol Meas,
    Doi: http://doi.org/10.1088/1361-6579/ad548e
  • Mathieu, AJW., Pascual, MS., Charlton, PH., Volovaya, M., Venton, J., Aston, PJ., Nandi, M. and Alastruey, J., 2024. Advanced waveform analysis of the photoplethysmogram signal using complementary signal processing techniques for the extraction of biomarkers of cardiovascular function. JRSM Cardiovasc Dis, v. 13
    Doi: http://doi.org/10.1177/20480040231225384
  • 2023 (Accepted for publication)

  • Charlton, P., 2023 (Accepted for publication). The 2023 wearable photoplethysmography roadmap Physiological Measurement,
    Doi: http://doi.org/10.1088/1361-6579/acead2
  • Hygrell, T. and Mant, J., 2023 (Accepted for publication). An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm ECGs facilitating screening Europace,
  • 2023

  • Alastruey, J., Charlton, PH., Bikia, V., Paliakaite, B., Hametner, B., Bruno, RM., Mulder, MP., Vennin, S., Piskin, S., Khir, AW., Guala, A., Mayer, CC., Mynard, J., Hughes, AD., Segers, P. and Westerhof, BE., 2023. Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet. Am J Physiol Heart Circ Physiol, v. 325
    Doi: http://doi.org/10.1152/ajpheart.00705.2022
  • Al-Halawani, R., Charlton, PH., Qassem, M. and Kyriacou, PA., 2023. A review of the effect of skin pigmentation on pulse oximeter accuracy. Physiol Meas, v. 44
    Doi: http://doi.org/10.1088/1361-6579/acd51a
  • Kyriacou, PA., Charlton, PH., Al-Halawani, R. and Shelley, KH., 2023. Inaccuracy of pulse oximetry with dark skin pigmentation: clinical implications and need for improvement. Br J Anaesth, v. 130
    Doi: http://doi.org/10.1016/j.bja.2022.03.011
  • 2023. Erratum: Vascular ageing: moving from bench towards bedside. Eur J Prev Cardiol, v. 30
    Doi: 10.1093/eurjpc/zwad134
  • Hong, J., Nandi, M., Charlton, PH. and Alastruey, J., 2023. Noninvasive hemodynamic indices of vascular aging: an in silico assessment. Am J Physiol Heart Circ Physiol, v. 325
    Doi: http://doi.org/10.1152/ajpheart.00454.2023
  • Zanelli, S., Eveilleau, K., Charlton, PH., Ammi, M., Hallab, M. and El Yacoubi, MA., 2023. Clustered photoplethysmogram pulse wave shapes and their associations with clinical data. Front Physiol, v. 14
    Doi: http://doi.org/10.3389/fphys.2023.1176753
  • Kotzen, K., Charlton, PH., Salabi, S., Amar, L., Landesberg, A. and Behar, JA., 2023. SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography. IEEE J Biomed Health Inform, v. 27
    Doi: 10.1109/JBHI.2022.3225363
  • Goda, MÁ., Charlton, PH. and Behar, JA., 2023. Robust peak detection for photoplethysmography signal analysis. ArXiv,
  • Rinkevičius, M., Charlton, PH., Bailón, R. and Marozas, V., 2023. Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography. Sensors (Basel), v. 23
    Doi: http://doi.org/10.3390/s23042220
  • Climie, RE., Alastruey, J., Mayer, CC., Schwarz, A., Laucyte-Cibulskiene, A., Voicehovska, J., Bianchini, E., Bruno, R-M., Charlton, PH., Grillo, A., Guala, A., Hallab, M., Hametner, B., Jankowski, P., Königstein, K., Lebedeva, A., Mozos, I., Pucci, G., Puzantian, H., Terentes-Printzios, D., Yetik-Anacak, G., Park, C., Nilsson, PM. and Weber, T., 2023. Vascular ageing: moving from bench towards bedside. Eur J Prev Cardiol, v. 30
    Doi: 10.1093/eurjpc/zwad028
  • 2022 (Accepted for publication)

  • Charlton, P., Kyriacou, P., Mant, J., Marozas, V., Chowienczyk, P. and Alastruey, J., 2022 (Accepted for publication). Wearable photoplethysmography for cardiovascular monitoring Proceedings of the Institute of Electrical and Electronics Engineers (IEEE),
  • Kyriacou, P., Charlton, P., Al-Halawani, R. and Shelley, K., 2022 (Accepted for publication). Inaccuracy of pulse oximetry with dark skin pigmentation: clinical implications and need for improvement British Journal of Anaesthesia,
    Doi: http://doi.org/10.1016/j.bja.2022.03.011
  • Charlton, P., Pilt, K. and Kyriacou, P., 2022 (Accepted for publication). Establishing best practices in photoplethysmography signal acquisition and processing Physiological Measurement,
  • Charlton, P., Kotzen, K., Mejia-Mejia, E., Aston, P., Budidha, K., Mant, J., Pettit, C., Behar, J. and Kyriacou, P., 2022 (Accepted for publication). Detecting beats in the photoplethysmogram: benchmarking open-source algorithms Physiological Measurement,
    Doi: http://doi.org/10.1088/1361-6579/ac826d
  • 2022

  • Adeniji, M., Brimicombe, J., Cowie, MR., Dymond, A., Linden, HC., Lip, GYH., Mant, J., Pandiaraja, M., Williams, K. and Charlton, PH., 2022. Prioritising electrocardiograms for manual review to improve the efficiency of atrial fibrillation screening. Annu Int Conf IEEE Eng Med Biol Soc, v. 2022
    Doi: http://doi.org/10.1109/EMBC48229.2022.9871092
  • 2021 (Accepted for publication)

  • Vennin, S., Li, Y., Mariscal-Harana, J., Charlton, P., Fok, H., Gu, H., Chowienczyk, P. and Alastruey, J., 2021 (Accepted for publication). Novel Pressure Wave Separation Analysis for Cardiovascular Function Assessment Highlights Major Role of Aortic Root IEEE Transactions on Biomedical Engineering,
  • Charlton, P., Paliakaite, B., Pilt, K., Bachler, M., Zanelli, S., Kulin, D., Allen, J., Hallab, M., Bianchini, E., Mayer, C., Terentes-Printzios, D., Dittrich, V., Hametner, B., Veerasingam, D., Zikic, D. and Marozas, V., 2021 (Accepted for publication). Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet American Journal of Physiology: Heart and Circulatory Physiology,
    Doi: 10.1152/ajpheart.00392.2021
  • Adami, A., Boostani, R., Marzbanrad, F. and Charlton, PH., 2021 (Accepted for publication). A New Framework to Estimate Breathing Rate from Electrocardiogram, Photoplethysmogram, and Blood Pressure Signals IEEE Access,
    Doi: http://doi.org/10.1109/access.2021.3066166
  • Bikia, V., Fong, T., Climie, R., Bruno, R-M., Hametner, B., Mayer, C., Terentes-Printzios, D. and Charlton, P., 2021 (Accepted for publication). Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research European Heart Journal: Digital Health,
  • 2021

  • Mariscal-Harana, J., Charlton, PH., Vennin, S., Aramburu, J., Florkow, MC., van Engelen, A., Schneider, T., de Bliek, H., Ruijsink, B., Valverde, I., Beerbaum, P., Grotenhuis, H., Charakida, M., Chowienczyk, P., Sherwin, SJ. and Alastruey, J., 2021. Estimating central blood pressure from aortic flow: development and assessment of algorithms. Am J Physiol Heart Circ Physiol, v. 320
    Doi: http://doi.org/10.1152/ajpheart.00241.2020
  • Viberg, F., Hygrell, T., Dahlberg, E., Charlton, P., Gudmundsdottir, KK., Mant, J., Hörnlund, JL. and Svennberg, E., 2021. B-PO05-147 AN ARTIFICIAL INTELLIGENCE-BASED MODEL FOR PREDICTION OF ATRIAL FIBRILLATION FROM SINGLE-LEAD SINUS RHYTHM ECGS ENABLING SCREENING Heart Rhythm, v. 18
    Doi: http://doi.org/10.1016/j.hrthm.2021.06.1066
  • Viberg, F., Hygrell, T., Dahlberg, E., Charlton, P., Gudmundsdottir, KK., Mant, J., Hörnlund, JL. and Svennberg, E., 2021. B-IN01-07 AN ARTIFICIAL INTELLIGENCE-BASED MODEL FOR PREDICTION OF ATRIAL FIBRILLATION FROM SINGLE-LEAD SINUS RHYTHM ECGS ENABLING SCREENING Heart Rhythm, v. 18
    Doi: http://doi.org/10.1016/j.hrthm.2021.06.1150
  • Li, Y., Guilcher, A., Charlton, PH., Vennin, S., Alastruey, J. and Chowienczyk, P., 2021. Relationship between fiducial points on the peripheral and central blood pressure waveforms: rate of rise of the central waveform is a determinant of peripheral systolic blood pressure. Am J Physiol Heart Circ Physiol, v. 320
    Doi: http://doi.org/10.1152/ajpheart.00818.2020
  • 2020 (Accepted for publication)

  • Charlton, P., Bonnici, T., Tarassenko, L., Clifton, D., Beale, R., Watkinson, P. and Alastruey, J., 2020 (Accepted for publication). An impedance pneumography signal quality index: design, assessment and application to respiratory rate monitoring Biomedical Signal Processing and Control,
  • 2020

  • Pandiaraja, M., Brimicombe, J., Cowie, M., Dymond, A., Lindén, HC., Lip, GYH., Mant, J., Williams, K. and Charlton, PH., 2020. Screening for Atrial Fibrillation: Improving Efficiency of Manual Review of Handheld Electrocardiograms †. Eng Proc, v. 2
    Doi: http://doi.org/10.3390/ecsa-7-08195
  • Charlton, PH., Kyriacou, P., Mant, J. and Alastruey, J., 2020. Acquiring Wearable Photoplethysmography Data in Daily Life: The PPG Diary Pilot Study †. Eng Proc, v. 2
    Doi: http://doi.org/10.3390/ecsa-7-08233
  • Celka, P., Charlton, PH., Farukh, B., Chowienczyk, P. and Alastruey, J., 2020. Influence of mental stress on the pulse wave features of photoplethysmograms. Healthc Technol Lett, v. 7
    Doi: http://doi.org/10.1049/htl.2019.0001
  • 2019

  • Jarchi, D., Charlton, P., Pimentel, M., Casson, A., Tarassenko, L. and Clifton, DA., 2019. Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry. Healthc Technol Lett, v. 6
    Doi: http://doi.org/10.1049/htl.2018.5019
  • Charlton, PH., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. and Alastruey, J., 2019. Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes. Am J Physiol Heart Circ Physiol, v. 317
    Doi: http://doi.org/10.1152/ajpheart.00218.2019
  • 2018 (Published online)

  • Tecelão, D. and Charlton, P., 2018 (Published online). Automated P-Wave Quality Assessment for Wearable Sensors 5th International Electronic Conference on Sensors and Applications,
    Doi: 10.3390/ecsa-5-05743
  • 2018

  • Charlton, PH., Willemet, M., Chowienczyk, P. and Alastruey, J., 2018. Comment on 'Numerical assessment and comparison of pulse wave velocity methods aiming at measuring aortic stiffness'. Physiol Meas, v. 39
    Doi: http://doi.org/10.1088/1361-6579/aaca80
  • Mathieu, A., Charlton, PH. and Alastruey, J., 2018. Using Smart Wearables to Monitor Cardiac Ejection †. Proceedings (MDPI), v. 4
    Doi: http://doi.org/10.3390/ecsa-5-05744
  • Dijab, H., Alastruey, J. and Charlton, PH., 2018. Measuring Vascular Recovery Rate After Exercise †. Proceedings (MDPI), v. 4
    Doi: http://doi.org/10.3390/ecsa-5-05746
  • Charlton, PH., Birrenkott, DA., Bonnici, T., Pimentel, MAF., Johnson, AEW., Alastruey, J., Tarassenko, L., Watkinson, PJ., Beale, R. and Clifton, DA., 2018. Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review. IEEE Rev Biomed Eng, v. 11
    Doi: http://doi.org/10.1109/RBME.2017.2763681
  • Pontoriero, AD., Charlton, PH. and Alastruey, J., 2018. Alzheimer's Disease: A Step Towards Prognosis Using Smart Wearables †. Proceedings (MDPI), v. 4
    Doi: http://doi.org/10.3390/ecsa-5-05742
  • Charlton, PH., Celka, P., Farukh, B., Chowienczyk, P. and Alastruey, J., 2018. Assessing mental stress from the photoplethysmogram: a numerical study. Physiol Meas, v. 39
    Doi: http://doi.org/10.1088/1361-6579/aabe6a
  • 2017

  • Charlton, PH., Bonnici, T., Tarassenko, L., Alastruey, J., Clifton, DA., Beale, R. and Watkinson, PJ., 2017. Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiol Meas, v. 38
    Doi: http://doi.org/10.1088/1361-6579/aa670e
  • Pimentel, MAF., Johnson, AEW., Charlton, PH., Birrenkott, D., Watkinson, PJ., Tarassenko, L. and Clifton, DA., 2017. Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters. IEEE Trans Biomed Eng, v. 64
    Doi: http://doi.org/10.1109/TBME.2016.2613124
  • Vennin, S., Li, Y., Willemet, M., Fok, H., Gu, H., Charlton, P., Alastruey, J. and Chowienczyk, P., 2017. Identifying Hemodynamic Determinants of Pulse Pressure: A Combined Numerical and Physiological Approach. Hypertension, v. 70
    Doi: http://doi.org/10.1161/HYPERTENSIONAHA.117.09706
  • 2016

  • Charlton, PH., Bonnici, T., Tarassenko, L., Clifton, DA., Beale, R. and Watkinson, PJ., 2016. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiol Meas, v. 37
    Doi: http://doi.org/10.1088/0967-3334/37/4/610
  • Aboab, J., Celi, LA., Charlton, P., Feng, M., Ghassemi, M., Marshall, DC., Mayaud, L., Naumann, T., McCague, N., Paik, KE., Pollard, TJ., Resche-Rigon, M., Salciccioli, JD. and Stone, DJ., 2016. A "datathon" model to support cross-disciplinary collaboration. Sci Transl Med, v. 8
    Doi: http://doi.org/10.1126/scitranslmed.aad9072
  • 2015

  • Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D. and Tarassenko, L., 2015. Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J Biomed Health Inform, v. 19
    Doi: http://doi.org/10.1109/JBHI.2014.2338351
  • 2012

  • Meredith, DJ., Clifton, D., Charlton, P., Brooks, J., Pugh, CW. and Tarassenko, L., 2012. Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. J Med Eng Technol, v. 36
    Doi: http://doi.org/10.3109/03091902.2011.638965
  • Charlton, R. and Charlton, P., 2012. A medical classic: Liza of Lambeth. Clin Med (Lond), v. 12
    Doi: http://doi.org/10.7861/clinmedicine.12-4-393
  • Conference proceedings

    2022 (Accepted for publication)

  • Adeniji, M., Brimicombe, J., Cowie, M., Dymond, A., Clair Linden, H., Lip, G., Mant, J., Pandiaraja, M., Williams, K. and Charlton, P., 2022 (Accepted for publication). Prioritising electrocardiograms for manual review to improve the efficiency of atrial fibrillation screening
  • 2022

  • Ahmadi, N., Al Farisyi, MS., Prihatmoko, MD., Hyanda, MH., Muhaimin, H., Mulyawan, R., Charlton, PH. and Adiono, T., 2022. Development and Evaluation of a Contactless Heart Rate Measurement Device Based on rPPG ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings,
    Doi: http://doi.org/10.1109/ICECS202256217.2022.9971006
  • 2021

  • Paliakaite, B., Charlton, PH., Rapalis, A., Plusciauskaite, V., Piartli, P., Kaniusas, E. and Marozas, V., 2021. Blood Pressure Estimation Based on Photoplethysmography: Finger Versus Wrist 2021 COMPUTING IN CARDIOLOGY (CINC),
    Doi: http://doi.org/10.22489/CinC.2021.020
  • Paliakaite, B., Charlton, PH., Rapalis, A., Plusciauskaite, V., Piartli, P., Kaniusas, E. and Marozas, V., 2021. Blood Pressure Estimation Based on Photoplethysmography: Finger Versus Wrist Computing in Cardiology, v. 2021-September
    Doi: http://doi.org/10.23919/CinC53138.2021.9662716
  • Kotzen, K., Charlton, PH., Landesberg, A. and Behar, JA., 2021. Benchmarking Photoplethysmography Peak Detection Algorithms Using the Electrocardiogram Signal as a Reference Computing in Cardiology, v. 2021-September
    Doi: http://doi.org/10.23919/CinC53138.2021.9662889
  • Kotzen, K., Charlton, PH., Landesberg, A. and Behar, JA., 2021. Benchmarking Photoplethysmography Peak Detection Algorithms Using the Electrocardiogram Signal as a Reference 2021 COMPUTING IN CARDIOLOGY (CINC),
    Doi: http://doi.org/10.22489/CinC.2021.088
  • 2019

  • Charlton, P., Aresu, M., Spear, J., Chowienczyk, P. and Alastruey, J., 2019. P7 Assessing Vascular Age from Peripheral Pulse Waves: a Study of Existing Indices, and Directions for Future Research Artery Research, v. 25
    Doi: 10.2991/artres.k.191224.042
  • 2018 (Published online)

  • Vennin, S., Li, Y., Willemet, M., Fok, H., Gu, H., Charlton, P., Alastruey, J. and Chowienczyk, P., 2018 (Published online). P32 DETERMINING CARDIAC AND ARTERIAL CONTRIBUTIONS TO CENTRAL PULSE PRESSURE Artery Research, v. 24
    Doi: 10.1016/j.artres.2018.10.085
  • Charlton, P., Aresu, M., Spear, J., Chowienczyk, P. and Alastruey, J., 2018 (Published online). P164 INDICES TO ASSESS AORTIC STIFFNESS FROM THE FINGER PHOTOPLETHYSMOGRAM: IN SILICO AND IN VIVO TESTING Artery Research, v. 24
    Doi: 10.1016/j.artres.2018.10.217
  • Mariscal Harana, J., Charlton, PH., Vennin, S., van Engelen, A., Schneider, T., Florkow, M., de Bliek, H., Ruijsink, B., Valverde, I., Charakida, M., Pushparajah, K., Sherwin, S., Botnar, R. and Alastruey, J., 2018 (Published online). P52 ESTIMATING CENTRAL BLOOD PRESSURE FROM MRI DATA USING REDUCED-ORDER COMPUTATIONAL MODELS Artery Research, v. 24
    Doi: 10.1016/j.artres.2018.10.105
  • 2018

  • Charlton, P., Chowienczyk, P. and Alastruey, J., 2018. An assessment of aortic stiffness indices using a model of healthy cardiovascular ageing JOURNAL OF HUMAN HYPERTENSION, v. 32
  • 2018. Abstracts from the 2018 Annual Scientific Meeting of the British and Irish Hypertension Society (BIHS). J Hum Hypertens, v. 32
    Doi: http://doi.org/10.1038/s41371-018-0109-3
  • 2017 (Published online)

  • Mariscal Harana, J., van Engelen, A., Schneider, T., Florkow, M., Charlton, P., Ruijsink, B., De Bliek, H., Valverde, I., Carakida, M., Pushparajah, K., Sherwin, S., Botnar, R. and Alastruey, J., 2017 (Published online). 3.6 NON-INVASIVE, MRI-BASED ESTIMATION OF PATIENT-SPECIFIC AORTIC BLOOD PRESSURE USING ONE-DIMENSIONAL BLOOD FLOW MODELLING Artery Research, v. 20
    Doi: 10.1016/j.artres.2017.10.036
  • Vennin, S., Li, Y., Willemet, M., Fok, H., Gu, H., Charlton, P., Alastruey, J. and Chowienczyk, P., 2017 (Published online). P121 IDENTIFYING HAEMODYNAMIC DETERMINANTS OF PULSE PRESSURE: AN INTEGRATED NUMERICAL AND PHYSIOLOGICAL APPROACH Artery Research, v. 20
    Doi: 10.1016/j.artres.2017.10.103
  • 2017

  • Lyle, JV., Charlton, PH., Bonet-Luz, E., Chaffey, G., Christie, M., Nandi, M. and Aston, PJ., 2017. Beyond HRV: Analysis of ECG signals using attractor reconstruction Computing in Cardiology, v. 44
    Doi: http://doi.org/10.22489/CinC.2017.091-096
  • van Engelen, A., Harana, JM., Schneider, T., Florkow, M., Charlton, P., Ruijsink, B., de Bliek, H., Valverde, I., Charakida, M., Pushparajah, K., Botnar, R. and Alastruey, J., 2017. Validation of Non-Invasive MRI-based Assessment of Central Blood Pressure in a Population of Repaired Coarctation Patients CIRCULATION, v. 136
  • 2016

  • Salciccioli, J., Charlton, P., Hartley, A., Komorowski, M., Marshall, D., Shalhoub, J., Sykes, MC. and Celi, L., 2016. Lactate Rebound As An Independent Predictor Of Mortality In Intensive Care AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, v. 193
  • 2015

  • Charlton, PH., Camporota, L., Smith, J., Nandi, M., Christie, M., Aston, PJ. and Beale, R., 2015. Measurement of cardiovascular state using attractor reconstruction analysis 2015 23rd European Signal Processing Conference, EUSIPCO 2015,
    Doi: http://doi.org/10.1109/EUSIPCO.2015.7362422
  • 2014

  • Charlton, P., Smith, J., Camporota, L., Beale, R. and Alastruey, J., 2014. Optimising the Windkessel model for cardiac output monitoring during changes in vascular tone. Annu Int Conf IEEE Eng Med Biol Soc, v. 2014
    Doi: http://doi.org/10.1109/EMBC.2014.6944441
  • 2012

  • Orphanidou, C., Bonnici, T., Vallance, D., Darrell, A., Charlton, P. and Tarassenko, L., 2012. A method for assessing the reliability of heart rates obtained from ambulatory ECG IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012,
    Doi: http://doi.org/10.1109/BIBE.2012.6399672
  • 2011

  • Smith, J., Scaramuzzi, M., Charlton, P., Brooks, J., Arces, D., Wong, G., Camporota, L. and Beale, R., 2011. COMPARISON OF NICOM BIOREACTANCE TECHNOLOGY :AND TRANSPULMONARY THERMODILUTION DURING NOREPINEPHRINE-DRIVEN CHANGE IN ARTERIAL BLOOD PRESSURE IN CRITICALLY ILL PATIENTS INTENSIVE CARE MEDICINE, v. 37
  • Smith, J., Scaramuzzi, M., Charlton, P., Brooks, J., Arces, D., Wong, G., Camporota, L. and Beale, R., 2011. PERFORMANCE OF TWO FLOTRAC-VIGILEO™ VERSIONS DURING NOREPINEPHRINE-DRIVEN CHANGE IN ARTERIAL BLOOD PRESSURE IN CRITICALLY ILL PATIENTS INTENSIVE CARE MEDICINE, v. 37
  • Smith, J., Scaramuzzi, M., Charlton, P., Brooks, J., Arces, D., Wong, G., Camporota, L. and Beale, R., 2011. EFFECTS OF NOREPINEPHRINE-DRIVEN CHANGE IN ARTERIAL BLOOD PRESSURE ON FOUR DIFFERENT CONTINUOUS CARDIAC OUTPUT SYSTEMS IN CRITICALLY ILL PATIENTS INTENSIVE CARE MEDICINE, v. 37
  • Book chapters

    2022

  • Mejía-Mejía, E., Allen, J., Budidha, K., El-Hajj, C., Kyriacou, PA. and Charlton, PH., 2022. 4 Photoplethysmography signal processing and synthesis
    Doi: http://doi.org/10.1016/b978-0-12-823374-0.00015-3
  • Charlton, PH. and Marozas, V., 2022. 12 Wearable photoplethysmography devices
    Doi: http://doi.org/10.1016/b978-0-12-823374-0.00011-6
  • 2021 (No publication date)

  • Mejia-Mejia, E., Allen, J., Budidha, K., El-Hajj, C., Kyriacou, P. and Charlton, P., 2021 (No publication date). Photoplethysmography Signal Processing and Synthesis
  • Charlton, P. and Marozas, V., 2021 (No publication date). Wearable Photoplethysmography Devices
  • 2016

  • Charlton, PH., Villarroel, M. and Salguiero, F., 2016. Waveform analysis to estimate respiratory rate
    Doi: http://doi.org/10.1007/978-3-319-43742-2_26
  • Charlton, PH., Pimentel, M. and Lokhandwala, S., 2016. Data fusion techniques for early warning of clinical deterioration
    Doi: http://doi.org/10.1007/978-3-319-43742-2_22
  • 2015

  • Pimentel, MAF., Charlton, PH. and Clifton, DA., 2015. Probabilistic estimation of respiratory rate from wearable sensors
    Doi: http://doi.org/10.1007/978-3-319-18191-2_10
  • Clifton, DA., Niehaus, KE., Charlton, P. and Colopy, GW., 2015. Health Informatics via Machine Learning for the Clinical Management of Patients.
    Doi: http://doi.org/10.15265/IY-2015-014
  • Reports

    2021

  • Charlton, P., 2021. Realising the potential of wearables
  • Books

    2016

  • Data, MITC., 2016. Secondary Analysis of Electronic Health Records