Skip to main content
Menu

Journal articles

[102] El-Bouri, R., Eyre, D., Watkinson, P., Zhu, T.T, and Clifton, D.A.:
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
International Conference on Machine Learning (ICML), PMLR (119), 2020 [PDF]

[101] Zhu, T.T., Watkinson, P., and Clifton, D.A.:
Smartwatch Data Help Detect COVID-19
Nature Biomedical Engineering, 2020 [In press]

[100] Ding, X., Clifton, D.A., Ji, N., Lovel, N.H., Bonata, P., Chen, W., Yu, X., Xue, Z., Xiang, T., Long., X., Xu, K., Jiang, X., Wang, Q., Yin, B, Feng, G., and Zhang, Y.T.:
Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
IEEE Reviews of Biomedical Engineering, 2020 [In press][Early PDF]

[99] Jiang, X., Xu, K., Dai, C., Clifton, D.A., Clancy, E.A., Akay, M., and Chen, W.:
Cancelable HD-sEMG-based Biometrics for Cross-Application Discrepant Personal Identification
IEEE Journal of Biomedical and Health Informatics, 2020 [In press][Early PDF]

[98] Manley, G.F., Pyle, D.M., Mather, T.A., Rodgers, M., Clifton, D.A., Stokell, B.G., Thompson, G., Londono, J.M., and Roman, D.C.:
Understanding the Timing of Eruption End using a Machine Learning Approach to Classification of Seismic Time Series
Journal of Volcanology and Geothermal Research 401, 2020, pp. 106917 [PDF]

[97] Shamout, F.E., Zhu, T.T., Sharma, P., Watkinson, P.J., and Clifton, D.A.:
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration
IEEE Journal of Biomedical and Health Informatics 24(2), 2020, pp. 437-446 [PDF]

[96] El-Bouri, R., Eyre, D., Watkinson, P., Zhu, T.T., Clifton, D.A.:
Hospital Admission Location Prediction via Deep Interpretable Networks for the Improvement of Emergency Patient Care
IEEE Journal of Biomedical and Health Informatics, 2020 [In press][Early PDF]

[95] Kiyasseh, D., Abebe Tadesse, G., Nhan, L.N.T., Tan, L.V., Thwaites, L., Zhu, T.T., and Clifton, D.A.:
PlethAugment: GAN-Based PPG Augmentation for Medical Diagnosis in Low-Resource Settings
IEEE Journal of Biomedical and Health Informatics 24(11), 2020, pp. 3226-3235 [PDF]

[94] Manandhar, A., Greeff, H., Thomson, P., Hope, R., and Clifton, D.:
Shallow Aquifer Monitoring Using Handpump Vibration Data
Journal of Hydrology X 8, 2020, pp. 100057 [PDF]

[93] Manandhar, A., Fischer, A., Bradley, D.J., Salehin, M., Sirajul Islam, M., Hope, R., and Clifton, D.A.:
Machine Learning to Evaluate Impacts of Flood Protection in Bangladesh, 1983-2014
Water 12(2), 2020, pp. 483 [PDF]

[92] Wilson, D.J. and the CRyPTIC Consortium (Kouchaki, S., Yang, Y., Lachapelle, A., and Clifton, D.A.):
GenomegaMap: Within-Species Genome-Wide dN/dS Estimation from over 10,000 Genomes
Molecular Biology and Evolution 37(8), 2020, pp. 2450-2460 [PDF]

[91] Charlton, P.H., Bonnici, T., Tarassenko, L., Clifton, D.A., Beale, R., Watkinson, P.J., and Alastruey, J.:
An Impedance Pneumography Signal Quality Index: Design, Assessment and Application to Respiratory Rate Monitoring
Biomedical Signal Processing and Control 65, 2020, pp. 102339 [PDF]

[90] Wong, D., Gerry, S., Shamout, F., Clifton, D.A., Pimentel, M.A.F., and Watkinson, P.:
Cross-Sectional Centiles of Blood Pressure by Age and Sex: A Four Hospital Database Retrospective Observational Analysis
BMJ Open 10, 2020, pp. e033618 [PDF]

[89] Kouchaki, S., Yang, Y., Walker, T.M., Walker, A.S., CRyPTIC Consortium, Peto, T.E.A., Crook, D.W., and Clifton, D.A.:
Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking
Frontiers in Microbiology 11, 2020, pp. 667 [PDF]

[88] Chen, W., Clifton, D.A., and Telfer, B.:
Guest Editorial: Integrative Sensor Networks, Informatics, and Modeling for Precision and Preventative Medicine
IEEE Journal of Biomedical and Health Informatics 24(7), 2020, pp. 1858-1859 [PDF]

[87] Shamout, F., Zhu, T.T., and Clifton, D.A.:
Machine Learning for Clinical Outcome Prediction
IEEE Reviews in Biomedical Engineering, 2020 [In press][Early PDF]

[86] Abebe Tadesse, G., Javed, H., Thanh, N.L.N., Ha, T.H.D., Le, V.T., Thwaites, L., Clifton, D.A., and Zhu, T.T.:
Multi-modal Diagnosis of Infectious Diseases in the Developing World
IEEE Journal of Biomedical and Health Informatics 24(7), 2020, pp. 2131-2141 [PDF]

[85] Duong, H.T.H., Abebe Tadesse, G., Nhat, P.T.H., Hao, N.V., Prince, J., Duong, T.D., Kien, T.T., Nhat, L.T.H., Van, T.L., Pugh, C., Loan, H.T., Van, V.C.N., Minh, Y.L., Zhu, T.T., Clifton, D.A., and Thwaites, L.:
Heart Rate Variability as an Indicator of Autonomic Nervous System Disturbance in Tetanus
American Journal of Tropical Medicine and Hygiene 102(5), 2020, pp. 403-407 [PDF]

[84] Abebe Tadesse, G., Zhu, T.T., Nhan, L.N.T., Nguyen, T.H., Ha, T.H.D., Truong, H.K., Pham, V.Q., Duc, D.T., Lam, M.Y., Van Doorn, H.R., Nguyen, V.H., Prince, J., Javed, H., Kiyasseh, D., Le, V.T., Thwaites, L., and Clifton, D.A.:
Severity Detection Tool for Patients with Infectious Disease
IET Healthcare Technology Letters 7(2), 2020, pp. 45-50 [PDF]

[83] Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D.A., Martini, C., and Morelli, D.:
A Public Dataset of 24-h Multi-levels Psycho-Physiological Responses in Young Healthy Adults
Data 5(4), 2020, pp. 91 [PDF]

[82] Leeming, G., Ainsworth, J., and Clifton, D.A.:
Blockchain in Health Care: Hype, Trust, and Digital Health
The Lancet 393, 2019, pp. 2476-2477 [PDF]

[81] Yang, Y., Walker, T.M., Walker, A.S., Wilson, D.J., Peto, T.E.A., Crook, D.W., Shamout, F., CRyPTIC Consortium, Zhu, T.T., and Clifton, D.A.:
DeepAMR for Predicting Co-occurrent Resistance of Mycobacterium Tuberculosis
Bioinformatics 35(18), 2019, pp. 3240-3249 [PDF]

[80] Zhu, T.T., Pimentel, M.A.F.,Clifford, G.D., and Clifton, D.A.:
Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalising Care
IEEE Journal of Biomedical and Health Informatics 23(1), 2019, pp. 47-58 [PDF]

[79] Colopy, G.W., Roberts, S.J., and Clifton, D.A.:
Gaussian Processes for Personalized Interpretable Volatilty Metrics in the Step-Down Ward
IEEE Journal of Biomedical and Health Informatics 23(3), 2019, pp. 949-959 [PDF]

[78] Zhu, T.T., Colopy, G.W., Yang, Y., Pugh, C.W., and Clifton, D.A.:
Patient-Specific Physiological Monitoring and Prediction Using Structured Gaussian Processes
IEEE Access 7(1), 2019, pp. 58094-58103 [PDF]

[77] Clifton, L., Birks, J., and Clifton, D.A.:
The Correlation Between Baseline Score and Post-intervention Score, and its Implications on Statistical Analysis
Trials 20(1), 2019, pp. 1-6 [PDF]

[76] Clifton, L., Birks, J., and Clifton, D.A.:
Comparing Different Ways of Calculating Sample Size for Two Independent Means: A Worked Example
Contemporary Clinical Trials Communications 13, 2019, pp. 100309 [PDF]

[75] Malycha, J., Farajidavar, N., Pimentel, M.A.F., Redfern, O., Clifton, D.A., Tarassenko, L., Meredith, P., Prytherch, D., Ludbrook, G., Young, D., and Watkinson, P.J.:
The Effect of Fractional Inspired Oxygen Concentration on Early Warning Score Performance: A Database Analysis
Resuscitation 139, 2019, pp. 192-199 [PDF]

[74] Kouchaki, S., Yang, Y., Walker, T.M., Walker, S., Wilson, D.J., Peto, T.E.A., Crook, D.W., and Clifton, D.A.:
Application of Machine Learning Techniques to Tuberculosis Drug Resistance Analysis
Bioinformatics 35(13), 2019, pp. 2276-2282 [PDF]

[73] Shamout, F., Zhu, T.T., Clifton, L., Briggs, J., Prytherch, D., Meredith, P., Tarassenko, L., Watkinson, P.J., and Clifton, D.A.:
Early Warning Score Adjusted for Age to Predict the Composite Outcome of Mortality, Cardiac Arrest or Unplanned Intensive Care Unity Admission Using Observational Vital-Sign Data: A Multicentre Development and Validation
BMJ Open 9, 2019, pp. e033301 [PDF]

[72] Sharma, P., Manandhar, A., Thomson, P., Katuva, J., Hope, R., and Clifton, D.A.:
Combining Multi-modal Statistics for Welfare Prediction Using Deep Learning
Sustainability 11(22), 2019, pp. 6312 [PDF]

[71] Malycha, J., Bonnici, T., Clifton, D.A., Ludbrook, G., Young, D., and Watkinson, P.J.:
Patient Centred Variables with Univariate Associations with Unplanned ICU Admission: A Systematic Review
BMC Medical Informatics and Decision Making 19(98), 2019, pp. 1-9 [PDF]

[70] Morelli, D., Bartolini, L, Rossi, A., and Clifton, D.A.:
A Computationally Efficient Algorithm to Obtain an Accurate and Interpretable Model of the Effect of Circadian Rhythm on Resting Heart Rate
Physiological Measurement 40(9), 2019, pp. 095001 [PDF]

[69] Morelli, D., Rossi, A., Cairo, M., and Clifton D.A.:
Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations
Sensors 19(14), 2019, pp. 3163 [PDF]

[68] Turner, H.C, Nguyen, V.H., Yacoub, S., Van, H.M.T., V.H., Clifton, D.A., Thwaites, G.E., Dondorp, A.M., Thwaites, C.L., and Nguyen, V.V.C.:
Achieving Affordable Critical Care in Low and Middle-Income Countries
BMJ Global Health 4(3), 2019, pp. 1-4 [PDF]

[67] Jarchi, D., Charlton, P., Pimentel, M.A.F., Casson, A.J., Tarassenko, L., and Clifton, D.A.:
Estimation of Respiratory Rate from Motion-Contaminated Photoplethysmography Signals Incorporating Accelerometry
IET Healthcare Technology Letters 6(1), 2019, pp.19-26 [PDF]

[66] Hill, N.R., Ayoubkhani, D., McEwan, P., Sugrue, D.M., Farooqui, U., Lister, S., Lumley, M., Bakhai, A., Cohen, A.T., O'Neill, M., Clifton, D.A., and Gordon, J.:
Predicting Atrial Fibrillation in Primary Care using Machine Learning
PLoS One 14(11), 2019, pp. e0224582 [PDF]

[65] Allix-Beguec, C., ..., Clifton, D.A., Yang, Y., ..., Zhu, B.:
Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing
New England Journal of Medicine 379(15), 2018, pp. 1403-1415 [PDF]

[64] Colopy, G.W., Roberts, S.J., and Clifton, D.A.:
Bayesian Optimisation of Personalised Models for Patient Vital-Sign Monitoring
IEEE Journal of Biomedical and Health Informatics 22(2), 2018, pp. 301-310 [PDF]
(Invited article)

[63] Luca, S., Pimentel, M.A.F., Watkinson, P.J., and Clifton, D.A.:
Point Process Models for Novelty Detection on Spatial Point Patterns and their Extremes
Computational Statistics and Data Analysis 125, 2018, pp. 86-103 [PDF]

[62] Yang, Y., Niehaus, K.E., Walker, T.M., Iqbal, Z., Walker, A.S., Wilson, D.J., Peto, T.E.A., Crook, D.W., and Clifton, D.A.:
Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data
Bioinformatics 34(10), 2018, pp. 1666-1671 [PDF]

[61] Yang, Y., Peng, Z., Dong, X., Zhang, W., and Clifton, D.A.:
Component Isolation for Multi-component Signal Analysis using a Non-parametric Gaussian Latent Feature Model
Mechanical Systems and Signal Processing 103, 2018, pp. 368-380 [PDF]

[60] Zhu, T.T., Yang, Y., Johnson, A.E.W., Clifford, G.D., and Clifton, D.A.:
Bayesian Fusion of Physiological Measurements using a Signal Quality Extension
Physiological Measurement 39(6), 2018, pp. 065008 [PDF]

[59] Birrenkott, D.A., Pimentel, M.A.F., Watkinson, P.J., and Clifton, D.A.:
A Robust Fusion Model for Estimating Respiratory Rate from Photoplethysmography and Electrocardiography
IEEE Transactions on Biomedical Engineering 65(9), 2018, pp. 2033-2041 [PDF]

[58] Greeff, H., Manandhar, A., Thomson, P., Hope, R., and Clifton, D.A.:
Distributed Inference Condition Monitoring System for Rural Infrastructure in the Developing World
IEEE Sensors Journal 19(5), 2018, 1820-1828 [PDF]

[57] Redfern, O.C., Pimentel, M.A.F., Prytherch, D., Meredith, P., Clifton, D.A., Tarassenko, L., Smith, G.B., and Watkinson, P.J.:
Predicting In-Hospital Mortality and Unanticipated Admissions to the Intensive Care Unit using Routinely Collected Blood Tests and Vital Signs: Development and Validation of a Multivariable Model
Resuscitation 133, 2018, pp. 75-81 [PDF]

[56] Watkinson, P.J., Pimentel, M.A.F., Clifton, D.A., and Tarassenko, L.:
Manual Centile-Based Early Warning Scores Derived from Statistical Distributions of Observational Vital-Sign Data
Resuscitation 129, 2018, pp. 55-60 [PDF]

[55] Jarchi, D., Salvi, D., Tarassenko, L., and Clifton, D.A.:
Validation of Instantaneous Respiratory Rate using Reflectance PPG from Different Body Positions
Sensors 18(11), 2018, pp. 3705 [PDF]

[54] Lee, R., Jarchi, D., Perera, R., Jones, A., Cassimjee, I., Handa, A., and Clifton, D.A.:
Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans
European Journal of Vascular and Endovascular Surgery Short Reports 39, 2018, pp. 24-28 [PDF]

[53] Jarchi, D., Rodgers, S.J., Tarassenko, L., and Clifton, D.A.:
Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring
IEEE Sensors Journal 18(12), 2018, pp. 4981-4989 [PDF]

[52] Charlton, P.H., Birrenkott, D.A., Bonnici, T., Pimentel, M.A.F., Johnson, A.E.W., Alastruey, J., Tarassenko, L., Watkinson, P.J., Beale, R., and Clifton D.A.:
Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram: A Review
IEEE Reviews in Biomedical Engineering 11, 2018, pp. 2-20 [PDF]
(Invited article)

[51] Morelli, D., Bartolini, L., Colombo, M., Plans, D., and Clifton, D.A.:
Profiling the Propagation of Error from PPG to HRV Features in a Wearable Physiological-Monitoring Device
IET Healthcare Technology Letters 5(2), 2018, pp. 59-64 [PDF]

[50] Tomlinson, H., Pimentel, M.A.F., Gerry, S., Clifton, D.A., Tarassenko, L., and Watkinson, P.J.:
Smoothing Effect in Vital-Sign Recordings: Fact or Fiction? A Retrospective Cohort Analysis of Manual and Continuous Vital-Sign Measurements to Assess Data Smoothing in Post-Operative Care
Anesthesia and Analgesia 127(4), 2018, pp. 960-966 [PDF]

[49] Colchester, F.E., Marais, H.G., Thomson, P., Hope, R., and Clifton, D.A.:
Accidental Infrastructure for Groundwater Monitoring in Africa
Environmental Modelling and Software 91, 2017, pp. 241-250 [PDF]

[48] Pimentel, M.A.F., Johnson, A.E.W., Charlton, P.H., Birrenkott, D.A., Watkinson, P.J., Tarassenko, L., and Clifton, D.A.:
Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters
IEEE Transactions on Biomedical Engineering 64(8), 2017, pp. 1914-1923 [PDF]

[47] Charlton, P.H., Bonnici, T., Tarassenko, L., Alastruey, J., Clifton D.A., Beale, R., and Watkinson, P.J.:
Extraction of Respiratory Signals from the Electrocardiogram and Photoplethysmogram: Technical and Physiological Determinants
Physiological Measurement 38, 2017, pp. 669-690 [PDF]

[46] Pullinger, R., Wilson, S., Way, R., Santos, M., Wong, D., Clifton, D.A., Birks, J., and Tarassenko, L.:
Implementing an Electronic Observation and Early Warning Score Chart in the Emergency Department: A Feasibility Study
European Journal of Emergency Medicine 24(6), 2017, pp. e11-e16 [PDF]

[45] Clifton, D.A.:
Case Studies of Medical Monitoring Systems
Equipment Health Monitoring in Complex Systems, Artech House, 2017, pp. 125-147 [PDF]

[44] King, S., Mills, A., Kadirkamanathan, V., and Clifton, D.A.:
Future Directions in Health Monitoring
Equipment Health Monitoring in Complex Systems, Artech House, 2017, pp. 177-203 [PDF]

[43] Luca, S., Clifton, D.A., and Vanrumste, B.:
One-Class Classification of Point Patterns of Extremes
Journal of Machine Learning Research 17(191), 2016, pp. 1-21 [PDF]

[42] Earle, S.G., Wu, C-H., Charlesworth, J., Stoesser, N., Gordon, N.C., Walker, T.M., Spencer, C.C.A., Iqbal, Z., Clifton, D.A., Smith, E.G., Ismail, N., Llewelyn, M.J., Peto, T.E.A., Crook, D.W., McVean, G., Walker, A.S., and Wilson, D.J.:
Identifying Lineage Effects When Controlling for Population Structure Improves Power in Bacterial Association Studies
Nature Microbiology 1(16041), 2016, DOI: 10.1038/NMICROBIOL.2016.41 [PDF]

[41] Johnson, A.E.W., Ghassemi, M.M., Nemati, S., Niehaus, K.E., Clifton, D.A., and Clifford, G.D.:
Machine Learning and Decision Support in Critical Care
Proceedings of the IEEE 104(2), 2016, pp. 444-466 [PDF]

[40] Wilson, S., Wong, D., Pullinger, R., Way, R., Clifton, D.A., and Tarassenko, L.:
Analysis of a Data Fusion System for Continuous Vital-Sign Monitoring in an Emergency Department
European Journal of Emergency Medicine 23(1), 2016, pp. 28-32 [PDF]

[39] Charlton, P.H., Bonnici, T., Tarassenko, L., Clifton D.A., Beale, R., and Watkinson, P.J.:
An Assessment of Algorithms for Estimation of Respiratory Rate from the Electrocardiogram and Photoplethysmogram
Physiological Measurements 37, 2016, pp. 610-626 [PDF]
(Awarded Institute of Physics Martin Black Prize)

[38] Niehaus, K.E. and Clifton, D.A.:
Machine Learning for Chronic Disease
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 227-250 [PDF]

[37] Yang, Y., Niehaus, K.E., and Clifton, D.A.:
Predicting Antibiotic Resistance from Genomic Data
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 203-226 [PDF]

[36] Zhu, T.T., Clifford, G.D., and Clifton, D.A.:
A Bayesian Model for Fusing Biomedical Labels
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 127-160 [PDF]

[35] Pimentel, M.A.F. and Clifton, D.A.:
Patient Physiological Monitoring with Machine Learning
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 111-126 [PDF]

[34] Clifton, D.A.:
Machine Learning for Healthcare Technologies: An Introduction
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET, 2016, pp. 1-6 [PDF]

[33] Walker, T.M., Kohl, T.A., Omar, S.V., Hedge, J., Del Oio Elias, C., Bradley, P., Iqbal, Z., Feuerriegel, S., Niehaus, K.E., Wilson, D.J., Clifton, D.A., Kapatai G., Ip, C., Bowden, R., Drobniewski, F., Allix-Beguec, C., Gaudin, C., Parkhill, J., Diel, R., Supply, P., Crook, D., Smith, E.G., Walker, A.S., Ismail, N., Nieman, S., and Peto, T.E.A.:
Whole-Genome Sequencing for Prediction of Mycobacterium Tuberculosis Drug-Susceptibility and Resistance: A Retrospective Cohort Study
Lancet Infectious Diseases 15(10), 2015, pp. 1193-1202 [PDF]

[32] Duerichen, R., Pimentel, M.A.F., Clifton, L., Schweikard, A., and Clifton, D.A.:
Multi-task Gaussian Processes for Multivariate Physiological Time-Series Analysis
IEEE Transactions on Biomedical Engineering 62(1), 2015, pp. 314-322 [PDF][Toolbox]

[31] Clifton, D.A., Clifton, L., Sandu, D.M., Smith, G.B., Tarassenko, L., Vollam, S., and Watkinson, P.J.:
"Errors" and Omissions in Paper-Based Early Warning Scores: The Association with Changes in Vital Signs - A Database Analysis.
British Medical Journal Open 5, e007376, 2015, pp. 1-7 [PDF]

[30] Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D.A., Vallance, D., and Tarassenko, L.:
Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring
IEEE Journal of Biomedical and Health Informatics 19(3), 2015, pp. 832-838 [PDF]

[29] Clifton, D.A., Niehaus, K.E., Charlton, P., and Colopy, G.W.:
Health Informatics via Machine Learning for the Clinical Management of Patients
Yearbook of Medical Informatics 10, 2015, pp. 38-43 [PDF]

[28] Clifton, D.A., Pimentel, M.A.F., Niehaus, K., Clifton, L., Peto, T.E.A., Crook, D.W., and Watkinson, P.J.:
Intelligent Electronic Health Systems
in "Telemedicine and Electronic Medicine", eds. Eren, H. and Webster, J.G.,
CRC Press, 2015, pp. 73-97 [PDF]

[27] Pimentel, M.A.F., Charlton, P.H., and Clifton, D.A.:
Probabilistic Estimation of Respiratory Rate from Wearable Sensors
in "Wearable Electronic Sensors", ed. Mukhopadhyay, S., Smart Sensors, Measurement and Instrumentation 15, 2015, pp. 241-262 [PDF]

[26] Zhu, T.T., Dunkley, N., Behar, J., Clifton, D.A., and Clifford, G.D.:
Fusing Continuous-Valued Medical Labels Using a Bayesian Model
Annals of Biomedical Engineering 43(12), 2015, pp. 2892-2902 [PDF]

[25] Clifton, D.A. and Tarassenko, L.:
Novelty Detection in Jet Engine Vibration Spectra
Int. Journal of Condition Monitoring 5(2), 2015, pp.2-7 [PDF]

[24] Clifton, L., Clifton, D.A., Farmery, A.D., and Hahn, C.E.W.:
A Non-invasive Method for Estimating Lung Function
Int. Journal of Condition Monitoring 5(3), 2015, pp.2-5 [PDF]

[23] Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., and Tarassenko, L.:
Predictive Monitoring of Mobile Patients by Combining Clinical Observations with Data from Wearable Sensors
IEEE Journal of Biomedical and Health Informatics 18(3), 2014, pp. 722-730 [PDF]
(Featured article for IEEE JBHI.)

[22] Clifton, D.A., Hugueny, S., Clifton, L., and Tarassenko, L.:
Extending the Generalised Pareto Distribution for Novelty Detection in High-Dimensional Spaces
Journal of Signal Processing Systems 74, 2014, pp. 323-339 [PDF]

[21] Pimentel, M.A.F., Clifton, D.A., Clifton, L., and Tarassenko, L.:
A Review of Novelty Detection
Signal Processing 99, 2014, pp. 215-249 [PDF] [Toolbox]

[20] Tarassenko, L., Villarroel, M., Guazzi, A., Jorge, J., Clifton, D.A., and Pugh, C.:
Non-contact Video-Based Vital Sign Monitoring Using Ambient Light and Auto-regressive Models
Physiological Measurement 35, 2014, pp. 807-831 [PDF]
(Awarded Institute of Physics Martin Black Prize)

[19] Clifton, L., Clifton, D.A., Zhang, Y., Watkinson, P.J., Tarassenko, L., and Yin, H:
Probabilistic Novelty Detection with Support Vector Machines
IEEE Transactions on Reliability 63(2), 2014, pp. 455-467 [PDF]

[18] Clifton, D.A., Clifton, L., Hugueny, S., Wong, D., and Tarassenko, L.:
An Extreme Function Theory for Novelty Detection
IEEE Journal of Selected Topics on Signal Processing 7(1), 2013, pp. 28-37 [PDF][CODE]

[17] Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., and Tarassenko, L.:
Gaussian Processes for Personalised e-Health Monitoring with Wearable Sensors
IEEE Transactions on Biomedical Engineering 60(1), 2013, pp. 193-197 [PDF]

[16] Clifton, L., Clifton, D., Hahn, C.E.W., and Farmery, A.D.
A Non-invasive Method for Estimating Cardiopulmonary Variables Using Breath-by-Breath Injection of Two Tracer Gases
IEEE Journal of Translational Engineering in Health and Medicine 1, 2013, pp. 1-8 [PDF]

[15] Clifton, L., Clifton, D., Hahn, C.E.W., and Farmery, A.D.:
Assessment of Lung Function Using a Non-invasive Oscillating Gas-Forcing Technique
Respiratory Physiology & Neurobiology, 189(1), 2013, pp. 174-182 [PDF]

[14] Clifton, D.A., Wong, D., Clifton, L., Pullinger, R., and Tarassenko, L.:
A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department
IEEE Journal of Biomedical & Health Informatics 17(4), 2013, pp. 835-842 [PDF]
(Awarded IMIA Prize, 2015)

[13] Pimentel, M.A.F., Clifton, D.A., Clifton, L., Watkinson, P.J., and Tarassenko, L.:
Modelling Physiological Deterioration in Post-operative Patient Vital-Sign Data
Medical & Biological Engineering & Computing 51, 2013, pp. 869-877 [PDF]

[12] Bonnici, T., Clifton, D.A., Watkinson, P.J., and Tarassenko, L.:
The Digital Patient
Clinical Medicine 13(3), 2013, pp. 252-257 [PDF]

[11] Wilson, S., Wong, D., Clifton, D.A., Fleming, S., Way, R., Pullinger, R., and Tarassenko, L.:
Track and Trigger in an Emergency Department: an Observational Evaluation Study.
Emergency Medicine Journal 30, 2013, pp. 186-191 [PDF]

[10] Khalid, S., Clifton, D.A., Clifton, L.A., and Tarassenko, L.:
A Two-Class Approach to the Detection of Physiological Deterioration in Patient Vital Signs, with Clinical Label Refinement
IEEE Transactions on Information Technology in Biomedicine 16(6), 2012, pp. 1231-1238 [PDF]

[9] Clifford, G.D. and Clifton, D.A.:
Annual Review: Wireless Technology in Disease State Management and Medicine
Annual Review of Medicine 63, 2012, pp. 479-492 [2010 Impact Factor 12.5] [PDF]

[8] Meredith, D., Clifton, D.A., Charlton, P., Brooks, J., Pugh, C.W., and Tarassenko, L.:
Photoplethysmographic Derivation of Respiratory Rate: A Review of Relevant Respiratory and Circulatory Physiology
Journal of Medical Engineering and Technology 36(1), 2012, pp. 60-66 [PDF]

[7] Tarassenko, L. and Clifton, D.A.:
Semiconductor Wireless Technology for Chronic Disease Management
Electronics Letters S30, 2011, pp. 30-32 [PDF]

[6] Tarassenko, L., Clifton, D.A., Pinsky, M.R., Hravnak, M.T., Woods, J.R., and Watkinson, P.:
Centile-Based Early Warning Scores Derived from Statistical Distributions of Vital Signs.
Resuscitation 82 (8), 2011, pp. 1013-1018 [PDF]

[5] Hugueny, S., Clifton, D.A., and Tarassenko, L.:
Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory
Communications in Computer Science 127, 2011, pp. 199-211 [PDF]

[4] Clifton, D.A., Hugueny, S., and Tarassenko, L.:
Novelty Detection with Multivariate Extreme Value Statistics
Journal of Signal Processing Systems 65, 2011, pp. 371-389 [PDF][CODE]

[3] King, S., Bannister, P.R., Clifton, D.A., and Tarassenko, L.:
Probabilistic Approaches to Condition Monitoring of Aerospace Engines
IMechE Part G: Journal of Aerospace Engineering 223 (G5), 2009, pp. 533-541 [PDF]

[2] Tarassenko, L., Clifton, D.A., Bannister, P.R., King, S., and King, D.:
Novelty Detection
Encyclopaedia of Structural Health Monitoring 2, Wiley, 2009, pp. 653-675 [PDF]

[1] Clifton, D.A., Clifton, L., Bannister, P.R., and Tarassenko, L.:
Automated Novelty Detection in Industrial Systems
Studies in Computational Intelligence 116, 2008, pp. 269-296 [PDF]