Signal analysis of physiological recordings for decision support and outcome prediction in the paediatric intensive care unit

In the intensive-care patient management, the routinely collected multi-parameter bedside physiological monitoring data that is available for clinical interpretation is under-used. Vital information is discarded rather than being optimally utilised for clinical decision making and outcome prediction, research, and quality improvement. Reasons for this data under-use include difficulty of accessing and using the data from its original sources, information overload from the plethora of routine data generated with advances in monitoring technologies, variability in clinical documentation methods and quality between different units. 

This PhD will address this problem by developing signal processing algorithms to extract clinically relevant information from the physiological recordings routinely collected at the bedside in paediatric intensive-care units. This will include methods to clean the signals, obtain relevant features, and finally predict outcomes.

To this end, fully anonymised routinely collected multi-parameter beside physiological monitoring data of critically ill infants and children who had sustained life threatening brain trauma from the KidsBrainIT data-bank will be used to develop the proof-of-concept decision support and outcome prediction tool. KidsBrainIT is a multi-centre, multi-disciplinary, multi-national data informatics paediatric brain trauma research initiative funded an EU grant (ERA-NET NEURON). In later phase of the PhD study, data from other critically ill infants and children are available for study to test the analytical decision making tool and its usefulness beyond life threatening brain trauma setting.

This project represents a unique opportunity for the successful candidate to be trained in an interdisciplinary area of research in collaboration with renowned clinical collaborators. The PhD will prepare the successful candidate for post-doctoral, more independent research in clinical applications of data analysis, a field that is expanding nowadays.

The physiological data used in this study is extremely rich. It includes a variety of cardiorespiratory, and neurological signals, thus enabling the inspection of patterns shared between them.  Physiological data of the brain trauma patients is linked with anonymised acute clinical data which includes injury mechanisms, severity, clinical treatment etc., and outcome assessed at six and twelve months post-injury.

Further Information: 

Early application is encouraged! Funding is already available and it may be allocated to the first suitable candidate.

Flexible starting date within 2018.

Closing Date: 

Wednesday, March 28, 2018
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Principal Supervisor: 


Candidates should have an MSc (or in exceptional cases an excellent Bachelor’s degree: 2:1 or higher) in electronic engineering, computer science (informatics), physics, mathematics or cognate areas. Prior experience in physiological signal and/or clinical data processing is desirable but not necessary. The candidate is expected to have good programming and analytical skills.

Further information on English language requirements for EU/Overseas applicants.


EPSRC funded (see EPSRC student eligibility). Tuition fees and stipend available for Home students or EU students who have been resident in the UK for 3 years (International students not eligible).

Further information and other funding options.

Informal Enquiries: