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Conference papers

[67] Zhu, T.T., Colopy, G.W., Pugh, C.W., and Clifton, D.A.:
Identifying Patient-Specific Trajectories in Haemodialysis using Bayesian Hierarchical Gaussian Processes
IEEE Biomedical and Health Informatics, Las Vegas, USA, 2018, pp. 186-189 [PDF]
(Best Paper prize)

[66] Jarchi, D., Salvi, D., Velardo, C., Mahdi, A., Tarassenko, L., and Clifton, D.A.:
Estimation of HRV and SpO2 from Wrist-Worn Commercial Sensors for Clinical Settings
IEEE Body Sensor Networks, Las Vegas, USA, 2018, pp. 144-147 [PDF]

[65] Jarchi, D., Mahdi, A., Tarassenko, L., and Clifton, D.A.:
Visualisation of Long-Term ECG Signals Applied to Post-Intensive Care Patients
IEEE Body Sensor Networks, Las Vegas, USA, 2018, pp. 165-168 [PDF]

[64] Colopy, G.W., Zhu, T.T., Clifton, L., Roberts, S.J., and Clifton, D.A.:
Likelihood-Based Artefact Detection in Continuously-Acquired Patient Vital Signs
IEEE Engineering in Medicine & Biology Conference, South Korea, 2017, pp. 2146-2149 [PDF]

[63] Colopy, G.W., Pimentel, M.A.F., Roberts, S.J., and Clifton, D.A.:
Bayesian Optimisation of Gaussian Processes for Identifying the Deteriorating Patient
IEEE Biomedical and Health Informatics, Orlando, Florida, USA, 2017, pp. 85-88 [PDF]

[62] Colopy, G.W., Pimentel, M.A.F., Roberts, S.J., and Clifton, D.A.:
Bayesian Gaussian Processes for Identifying the Deteriorating Patient
IEEE Engineering in Medicine & Biology Conference, Orlando, Florida, USA, 2016, pp. 5311-5314 [PDF]

[61] Birrenkott, D.A., Pimentel, M.A.F., Watkinson, P.J., and Clifton, D.A.:
Robust Estimation of Respiratory Rate via ECG- and PPG-Derived Respiratory Quality Indices
IEEE Engineering in Medicine & Biology Conference, Orlando, Florida, USA, 2016, pp. 676-679 [PDF]

[60] Shen, Y., Yang, Y., Parish, S., Chen, Z., Clarke, R., and Clifton, D.A.:
Risk Prediction for Cardiovascular Disease using ECG Data in the China Kadoorie Biobank
IEEE Engineering in Medicine & Biology Conference, Orlando, Florida, USA, 2016, pp. 2419-2422 [PDF]

[59] Zhu, T.T., Pimentel, M.A.F., Clifford, G.D., and Clifton, D.A.:
Bayesian Fusion of Algorithms for the Robust Estimation of Respiratory Rate from the Photoplethysmogram
IEEE Engineering in Medicine & Biology Conference, Milan, Italy, 2015, pp. 6138-6141 [PDF][Poster]

[58] Niehaus, K.E., Uhlig, H.H., and Clifton, D.A.:
Phenotypic Characterisation of Crohn's Disease Severity
IEEE Engineering in Medicine & Biology Conference, Milan, Italy, 2015, pp. 7023-7026 [PDF]

[57] Ghassemi, M., Pimentel, M.A.F., Naumann, T., Brennan, T., Clifton, D.A., Szolovits, P., and Feng, M.:
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data
AAAI Conference on Artificial Intelligence, Texas, USA, 2015, pp. 446-453 [PDF]

[56] Duerichen, R., Wissel, T., Ernst, F., Pimentel, M.A.F., Clifton, D.A., and Schweikard, A.:
Unified Approach for Respiratory Motion Prediction and Correlation with Multi-task Gaussian Processes
IEEE Machine Learning for Signal Processing, Reims, France, 2014, pp. 1-6 [PDF][Toolbox]

[55] Johnson, A.E.W., Burgess, J., Pimentel, M.A.F., Clifton, D.A., Young, D., Watkinson, P.J., and Tarassenko, L.:
Physiological Trajectory of Patients Pre and Post ICU Discharge
IEEE Engineering in Medicine and Biology Conference, Chicago, USA, 2014, pp. 3160-3163 [PDF]

[54] Zhu, T., Osipov, M., Papastylianou, T., Oster, J., Clifton, D.A., and Clifford, G.D.:
An Intelligent Cardiac Health Monitoring and Review System
IET Appropriate Healthcare Technologies (AHT), London, 2014, pp. 1-4 [PDF]

[53] Pimentel, M.A.F., Santos, M.D., Maraci, M.A., Arteta, C., Domingos, J.D., Clifton, D.A., and Clifford, G.D.:
A $5 Smart Blood Pressure System
IET Appropriate Healthcare Technologies (AHT), London, 2014, pp. 1-4 [PDF]

[52] Fathima, S., Palmius, N., Clifton, D.A., Hall-Clifford, R., and Clifford, G.D.:
Sanitation-Related Disease Surveillance using Community Health Promoters and Mobile Phone Technology
IET Appropriate Healthcare Technologies (AHT), London, 2014, pp. 1-4 [PDF]

[51] Colchester, F.E., Greeff, H., Thomson, P., Hope, R., and Clifton, D.A.:
Smart Handpumps: A Preliminary Data Analysis
IET Appropriate Healthcare Technologies (AHT), London, 2014, pp. 1-4 [PDF]

[50] Xue, Y.Y., Li, Q., Jin, L., Feng, L., Clifton, D.A., and Clifford, G.:
Detecting Adolescent Psychological Pressures from Micro-Blogs
Health Information Systems (HIS), Shenzhen, China, 2014, LNCS (8423), pp. 83-94 [PDF]

[49] Duerichen, R., Pimentel, M.A.F., Clifton, L., Schweikard, A., and Clifton, D.A.:
Multi-task Gaussian Process Models for Biomedical Applications
IEEE Biomedical & Health Informatics, Valencia, Spain, 2014, pp. 492-495 [PDF][Toolbox]

[48] Niehaus, K.E., Walker, T.M., Crook, D.W., Peto, T.E.A., and Clifton, D.A.:
Machine Learning for the Prediction of Mycobacterium Tuberculosis Antibacterial Susceptibility
IEEE Biomedical & Health Informatics, Valencia, Spain, 2014, pp. 618-621 [PDF]

[47] Pimentel, M.A.F., Clifton, D.A., Clifton, L., and Tarassenko, L.:
Modelling Patient Time-Series Data from Electronic Health Records using Gaussian Processes,
NIPS Workshop on Machine Learning for Clinical Data Analysis, Lake Tahoe, 2013, pp. 1-4 [PDF]

[46] Pimentel, M.A.F., Clifton, D.A., and Tarassenko, L.:
Gaussian Process Clustering for the Functional Characterisation of Vital-Sign Trajectories
IEEE Machine Learning for Signal Processing, Southampton, UK, 2013, pp. 1-6 [PDF]

[45] Khalid, S., Clifton, D.A., and Tarassenko, L.:
A Bayesian Patient-Based Model for Detecting Deterioration in Vital Signs using Manual Observations
Foundations of Health Information Engineering and Systems (FHIES), Macau, 2013, Lecture Notes in Computer Science (8315), Springer-Verlag, pp. 146-158 [PDF]

[44] Santos, M., Clifton, D.A., and Tarassenko, L.:
Performance of Early Warning Scoring Systems to Detect Patient Deterioration in the Emergency Department
Foundations of Health Information Engineering and Systems (FHIES), Macau, 2013, Lecture Notes in Computer Science (8315), Springer-Verlag, pp. 159-169 [PDF]

[43] Xue, Y.Y., Li, Q., Feng, L., Clifford, G.D., and Clifton, D.A.:
Towards a Micro-Blog Platform for Sensing and Easing Adolescent Psychological Pressures
Int. Conf. Pervasive & Ubiquitous Computing (UbiComp), Zurich, Switzerland, 2013, pp. 215 - 218 [PDF]

[42] Pimentel, M.A.F., Clifton, D.A., Clifton, L., and Tarassenko, L.:
Probabilistic Estimation of Respiratory Rate using Gaussian Processes
IEEE Engineering in Medicine and Biology Conference, Osaka, Japan, 2013, pp. 2902 - 2905 [PDF]

[41] Wong, D., Clifton, D.A., and Tarassenko, L.:
Probabilistic Detection of Vital Sign Abnormality with Gaussian Process Regression
IEEE Bioinformatics and Bioengineering, Cyprus, 2012, pp. 187-192 [PDF]

[40] Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., and Tarassenko, L.:
Gaussian Process Regression in Vital-Sign Early Warning Systems
IEEE Engineering in Medicine and Biology Conference, San Diego, USA, 2012, pp. 6161 - 6164 [PDF]

[39] Clifton, D.A., Gibbons, J., Davies, J., and Tarassenko, L.:
Machine Learning and Software Engineering in Health Informatics
RAISE Workshop, IEEE Int. Conf. on Software Engineering, Zurich, 2012, pp. 37 - 41 [PDF]

[38] Pimentel, M.A.F., Clifton, D.A., Clifton, L.A., Watkinson, P.J., and Tarassenko, L.:
Vital-Sign Data Fusion Models for Post-operative Patients
Biomedical Engineering Systems and Technologies, Algarve, Portugal, 2012, pp. 410-413 [PDF]

[37] Tarassenko, L., Clifton, D.A., Gibson, O., Villaroel, M., Watkinson, P.J., and Farmer, A.:
Integration of Wireless Health Technology within the UK Healthcare System
Wireless Health, San Diego, USA, 2012 [PDF]

[36] Clifton, D.A., Hugueny, S., and Tarassenko, L.:
Pinning the Tail on the Distribution: A Multivariate Extension to the Generalised Pareto Distribution
IEEE Machine Learning for Signal Processing, Beijing, China, 2011, pp. 1 - 6 [PDF] [CODE]
(Selected for journal publication)

[35] Clifton, L.A., Clifton, D.A., Watkinson, P.J., and Tarassenko, L.:
Identification of Patient Deterioration in Vital-Sign Data using One-Class Support Vector Machines
Artificial Intelligence in Medical Applications, Poland, 2011, pp. 125 - 131 [PDF]

[34] Clifton, D.A., Wong, D., Fleming, S., Wilson, S., Way, R., Pullinger, R., and Tarassenko, L.:
Novelty Detection for Identifying Deterioration in Emergency Department Patients
Intelligent Data Engineering and Automated Learning, Norwich, UK, 2011.
in Yin, H., Wang, W., and Rayward-Smith, V. (eds): Lecture Notes in Computer Science 6936
Springer-Verlag, Heidelberg, 2011, pp. 220-227 [PDF]

[33] Chykeyuk, K., Clifton, D.A., and Noble, JA.:
Feature Extraction and Wall Motion Classification of 2D Stress Echocardiography with Relevance Vector Machines
IEEE International Symposium on Biomedical Imaging, Chicago, USA, 2011, pp. 677-680 [PDF]

[32] Clifton, D.A., Clifton, L.A., Alvi, M., Khalid, S., Meredith, D., Price, J., Watkinson, P., and Tarassenko, L.:
Towards Assisted Living via Probabilistic Vital-Sign Monitoring in the Home
IET Assisted Living, London, UK, 2011, pp. 9-14 [PDF]

[31] Chykeyuk, K., Clifton, D.A., and Noble, JA.:
Support Vector Machines for Wall Motion Classification of 2-D Stress Echocardiography
Summers, R.M. and van Ginnekan, B. (eds): SPIE Medical Imaging 7963, 2011, pp. 16-22 [PDF]

[30] Khalid, S., Clifton, D.A., Clifton, L.A., and Tarassenko, L.:
Optimising Classifiers for the Detection of Physiological Deterioration in Patient Vital-Sign Data
Biomedical Engineering Systems and Technologies, Rome, Italy, 2011, pp. 425-428 [PDF]

[29] Khalid, S., Clifton, D.A., and Tarassenko, L.:
Modelling Patient Vital-Sign Deterioration Trajectories Using Bayesian Inference
IEEE PGBIOMED, Glasgow, UK, 2011, pp. 1-2 [PDF]

[28] Clifton, D.A., Meredith, D., Borhani, Y., Price, J., Pugh, C., and Tarassenko, L.:
Probabilistic Early Warning Systems for Detecting Patient Deterioration in the Home
Advances in Digital Healthcare: Telehealth and Mobile Health, Warwick, UK, 2010, pp. 3-4 [PDF]

[27] Borhani, Y., Fleming, S., Clifton, D.A., Sutherland, S., Hills, L., Meredith, D., Pugh, C., and Tarassenko, L.:
Towards a Data Fusion Model for Predicting Deterioration in Dialysis Patients
Computing in Cardiology (37), Belfast, UK, 2010, pp. 967-970 [PDF]

[26] Hugueny, S., Clifton, D.A., and Tarassenko, L.:
Understanding Vital-Sign Abnormalities in Critical Care Patients
Critical Care 14(1), 2010, pp. 51-52 [PDF]

[25] Sutherland, S., Hills, L., Meredith, D., Borhani, Y., Clifton, D.A., Fleming, S., Mosson, A., Thornley, A., Tarassenko, L., and Pugh, C.:
Integrated Vital Sign Monitoring of Haemodialysis Patients: Pilot Study
39th European Renal Care Association Conference, Dublin, Ireland, 2010 [PDF]
(Best Paper prize)

[24] Meredith, D., Borhani, Y., Sutherland, S., Hills, L., Fleming, S., Clifton, D.A., Thornley, A., Tarassenko, L., and Pugh, C.:
Monitoring of Vital Signs During Haemodialysis
British Renal Association Conference, Manchester, UK, 2010, pp. 355 [PDF]

[23] Hugueny, S., Clifton, D.A., and Tarassenko, L.:
Probabilistic Patient Monitoring Using Extreme Value Theory
Biomedical Engineering Systems and Technologies, Valencia, Spain, 2010, pp. 5-12 [PDF]
(Abbey-Santander prize, BIOSTEC Best Overall Paper prize, IET William James Award)

[22] Sundaram, S., Clifton, D.A., and McDonald, K.:
Stable Distributions for Heavy-Tailed Data and their Application in Asset Health Monitoring
Condition Monitoring, Stratford, UK, 2010, pp. 919-930 [PDF]

[21] Clifton, D.A., Hugueny, S., and Tarassenko, L.:
A Comparison of Approaches to Multivariate Extreme Value Theory for Novelty Detection
IEEE Statistical Signal Processing, Cardiff, UK, 2009, pp. 13-16 [PDF]

[20] Clifton, D.A., Hugueny, S., and Tarassenko, L.:
Novelty Detection with Multivariate Extreme Value Theory,Part I:
A Numerical Approach to Multimodal Estimation
IEEE Machine Learning for Signal Processing, Grenoble, France, 2009, pp. 1-6 [PDF]
(Selected for journal publication)

[19] Hugueny, S., Clifton, D.A., and Tarassenko, L.:
Novelty Detection with Multivariate Extreme Value Theory, Part II:
An Analytical Approach to Unimodal Estimation
IEEE Machine Learning for Signal Processing, Grenoble, France, 2009, pp. 1-6 [PDF]
(Selected for journal publication)

[18] Orphanidou, C., Clifton, D.A., Smith, M., Feldmar, J., and Tarassenko, L.:
Telemetry-Based Vital-Sign Monitoring for Ambulatory Hospital Patients
IEEE Engineering in Medicine and Biology Conference, Minneapolis, USA, 2009, pp. 4650-4653 [PDF]

[17] Clifton, D.A., Clifton, L.A., and Tarassenko, L.:
Patient-Specific Biomedical Condition Monitoring for Post-operative Cancer Patients
Condition Monitoring, Dublin, Ireland, 2009, pp. 424-433 [PDF]

[16] Clifton, D.A. and Tarassenko, L:
Novelty Detection in Jet Engine Vibration Spectra
Condition Monitoring, Dublin, Ireland, 2009, pp. 727-738 [PDF]

[15] Strachan, I.G.D and Clifton, D.A.:
A Hidden Markov Model for Condition Monitoring of a Manufacturing Drilling Process
(IET) Condition Monitoring, Dublin, Ireland, 2009, pp. 803-814 [PDF]

[14] Sundaram, S., Clifton, D.A., Strachan, I.G.D., Tarassenko, L., and King, S.:
Aircraft Engine Health Monitoring with Density Modelling and Extreme Value Statistics
Condition Monitoring, Dublin, Ireland, 2009, pp. 919-930 [PDF]

[13] Wong, D., Clifton, D.A., and Tarassenko, L.:
An Introduction to the Bispectrum for EEG Analysis
IEEE PGBIOMED, Oxford, UK, 2009, pp. 61-62 [PDF] [Proceedings]

[12] Clifton, D.A., McGrogan, N., Tarassenko, L., King, S., Anuzis, P., and King, D.:
Bayesian Extreme Value Statistics for Novelty Detection in Gas-Turbine Engines
IEEE Aerospace, Montana, USA, 2008, pp. 1-11 [PDF]

[11] Clifton, D.A., Bannister, P.R., Clifton, L.A., Sundaram, S., King, S., and Tarassenko, L.:
High Dimensional Visualisation for Novelty Detection
Condition Monitoring, Edinburgh, U.K., 2008, pp. 262-272 [PDF]

[10] Clifton, D.A., Tarassenko, L., Sage, C., and Sundaram, S.:
Condition Monitoring of Manufacturing Processes
Condition Monitoring, Edinburgh, U.K., 2008, pp. 273-279 [PDF]
(Best Student Paper Prize)

[9] Sundaram, S., Strachan, I.G.D., Clifton, D.A., King, S., and Palmer, J.:
A Data Mining Approach to Reveal Patterns in Aircraft Engine and Operational Data
Condition Monitoring, Edinburgh, U.K., 2008, pp. 1387-1397 [PDF]

[8] Bannister, P.R., Clifton, D.A., and Tarassenko, L.:
Constructing and Retraining Models for Condition Monitoring in Jet Engines
Condition Monitoring, Edinburgh, U.K., 2008, pp. 110-117 [PDF]

[7] Clifton, D.A., Haskins, B., Bannister, P.R., and Tarassenko, L.:
Specific and Generic Models for Jet Engine Novelty Detection
Condition Monitoring, Harrogate, UK, 2007, pp. 478-487 [PDF]

[6] Utete, S.W., Clifton, D.A., and Tarassenko, L.:
Trending Performance Parameters for Aircraft Condition Monitoring
Condition Monitoring, Harrogate, UK, 2007, pp. 1949-1955 [PDF]

[5] Clifton, L.A., Yin, H., Clifton, D.A., and Zhang, Y.:
Combined Support Vector Novelty Detection for Multi-channel Combustion Data
IEEE Sensors, Networks, and Control, London, UK, 2007, pp. 495-500 [PDF]

[4] Clifton, D.A., Bannister, P.R., and Tarassenko, L.:
A Framework for Novelty Detection in Jet Engine Vibration Data
Key Engineering Materials 347, 2007, pp. 305-312 [PDF]

[3] Clifton, D.A., Bannister, P.R., and Tarassenko, L.:
Novelty Detection in Large-Vehicle Turbochargers
Okuno, H.G. and Ali, M. (eds): New Trends in Applied Artificial Intelligence.
Springer-Verlag, Heidelberg, 2007, pp. 591-600 [PDF]
(Int. Soc. AI Best Paper Award)

[2] Clifton, D.A., Bannister, P.R., and Tarassenko, L.
Application of an Intuitive Novelty Metric for Jet Engine Condition Monitoring
Ali, M. and Dapoigny, R. (eds.): Advances in Applied Artificial Intelligence.
Springer-Verlag, Heidelberg, 2006, pp. 1149-1158 [PDF]

[1] Clifton, D.A., Bannister, P.R., and Tarassenko, L.:
Learning Shape for Jet Engine Novelty Detection
Wang, J. et al. (eds.): Advances in Neural Networks III.
Springer-Verlag, Berlin, 2006, pp. 828-835 [PDF]