Survival rates after cardio respiratory arrest and CPR are low.  In hospital, the chance of surviving to discharge is 15-20%; out of hospital, the chance of surviving is lower at 5-10%. There is a need to improve access to AED’s and also deliver improved diagnostics on site to able best mange the patient to a successful outcome. This project will address the feasibility of intelligent processing of the data from rapid (less than 5 mins.) from high sensitivity H-FABP diagnostic sensors. H-FABP is a highly sensitive early-rise marker of acute coronary syndrome (ACS), detectable as early as 30 minutes following the onset of an ischemic episode.

This will allow higher quality management of CPR data. In order to develop this, data will be collected via our own FDA approved, specially developed wireless integrated devices, used impedimetric /optical transducers, which will feed data to a central encrypted secure system. This will be continuously processed and algorithms will be developed to allow predictive / trending scenarios against the state of the patient’s health. All of this development will allow a responder performing CPR/defibrillation, to better define the condition of the patient before entering a hospital, thus enhancing the unique attributes of such a product via improved patient and cost saving benefits.


The project will attempt to identify the use of Biomarker devices and associated multiple datasets, to provide improved decision making (with the help of machine learning), alerts and management at the CPR stages through to hospitalisation. The specific aims are to:

1) set-up and integrate sensing technology to specifically focus on CPR;

2) to perform key data-analysis of cardiac enzyme studies, in order to assess how key algorithms could advice at specific steps of the CPR procedure;

3) to determine the feasibility of high-resolution collection of biomarker data such as h-FABP data to improve diagnostics, alerts and early-warning during the survival period by producing predictive trends against previous datasets thus allowing high levels of fast and accurate determination of the nature of the event. We have expertise and access to fabrication and assembly of these sensing platforms.

Outputs - scientific and impact:

Academic. This study will provide a pathway to the development of new miniaturised, easy to use smart CPR diagnostics that will allow a clinician and eventually a carer/responder to train better, use an integrated device on-site for improved care and most importantly early warning of key changes in cardiac function during transportation to hospital. Importantly this will allow prioritising of patients who have had an event ranging from cardiac arrest or chest pain to Myocardial infarction. Such a device will be low-cost, produce decisions within 5 minutes. The device would be stored alongside AED’s for emergency use and also be core to feedback on new procedures associated the full CPR survival phase.

Skills – either data analytics, computer science, materials science  or electrical engineering

Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.


This project is funded by: the EU INTERREG VA Programme

This project is supported by the European Union's INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB).

The scholarships will cover tuition fees and a maintenance award of not less than £14,553 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.

Other information

The Doctoral College at Ulster University