PhD Programmes with ECME

Main Supervisor: Prof Jim McLaughlin (PI & Partner Lead)

Duration: 3

The project will attempt to identify the use of cardiac enzyme devices and associated wearable based multiple ecg sensors, to provide improved decision making, alerts and management at the CPR stages through to hospitalisation.

Main Supervisor: Dr Dewar Finlay

Duration: 3

Whilst there has already been much innovation in the development of new methods for the detection and treatment of cardiovascular disease emerging technologies are providing the catalyst for further significant development. This project will exploit large datasets of multimodal cardiovascular patient data to develop tools to support the rapid diagnosis of cardiovascular disease. Analysis will focus on the application of emerging techniques to composite datasets that consists of parameters that include vital signs, cardiac biomarkers and medical imagery.

Duration: 3

Fabrication of bioresorbable polymer substrates via 3D Bioprinting methods that can support the adhesion, proliferation and differentiation of cardiomyocytes; Integration of sensor components capable of monitoring the response cardiomyocytes to external stimulation; Application of testing methods to predict the efficacy of the scaffold based myocardial in vitro model system for AF detection.

Duration: 3

The aim of the project is to improve the usability experience of home based users rehabilitating post stroke through usage of un-obtrusive sensing platforms and to embed intelligence in self-reporting solutions to improve levels of technology adoption. This Project will be the first of its kind to contribute to the domain of un-obtrusive sensing within the home environment for those recovering post stoke. In addition, it will be the first Project of its kind to embed intelligence in the self-management of home based rehabilitation through alignment with the key stages of behavior change strategies.

Main Supervisor: Dr Fergal McCaffery

Duration: 4

This research will focus on the development of an approach that can assist Healthcare Delivery Organisations and Medical Device Manufacturers to implement the requirements of standards and regulations related to risks when placing devices into IT networks.

Main Supervisor: Rodd Bond (PL) Dr Julie Doyle

Duration: 4

This research will explore design strategies to implement multiple behavior change interventions in older people with heart conditions and co-morbidities. It will examine the effectiveness of these techniques over a trial period and investigate the impact of the technology on supporting / inhibiting behavior change.

Main Supervisor: Dr John Loane

Duration: 4

This research project will investigate the extent to which multi-modal ambient sensing and data analysis can track cardiac patients’ adherence to dietary and physical activity recommendations to improve their health and wellbeing in the home setting.

Main Supervisor: Dr Niamh Caprani

Duration: 4

This research will develop training models to help older adults to use ICT-based monitoring technologies at home to self-manage their cardiac conditions, addressing older learner’s capabilities, environment, family, community and healthcare provision context.

Main Supervisor: Dr Lucia Carragher

Duration: 4

This research will investigate strategies to transform community care for older people with cardiac conditions, supporting the development of a workforce skilled in person centred, technology enabled care, and informing the workforce development policy-making process.

Main Supervisor: Prof Brian Caulfield (PL)

Duration: 4

In this project, we will design and develop a knowledge discovery platform and study the effectiveness of multimodal data warehouse and data mining techniques on the accuracy, robustness, and scalability of the results of shared cardiology datasets and their sources. The techniques that will be used will take into account the feedback as an input for future use. That feedback will be given in a form of knowledge that has been already either extracted from the data or given in a form of initial repository. This project will explore new approaches of representing and modelling (like knowledge maps) the knowledge which can exploited to improve the speed-up, accuracy and reliability of the results.

Main Supervisor: Dr David Coyle

Duration: 4

This project will focus on the design, development and evaluation of mobile applications that embody differing theoretical approaches to behaviour change and coaching. It will focus on physical and mental wellbeing and will provide adaptive, personalised support for patients with cardiac conditions. The apps will take advantage of active and passive data collection and will be developed using a user-centred design methodology, involving close collaborations with representative stakeholders.

Main Supervisor: Dr Brian MacNamee

Duration: 4

This project will focus on the development of models that use patient generated data to recognise cardiac patient behaviour and lifestyle patterns in the home and community, and identify factors that influence changes in these patterns. The project will be based on the creation and analysis of a longitudinal patient-generated dataset that leverages mobile devices and sensors.

Main Supervisor: Dr David McEneaney

Duration: 3

In the UK there are over 10 000 in-hospital cardiac arrests per year [1]. High quality cardiopulmonary resuscitation training for hospital staff has been shown to increase patient survival from arrest to hospital discharge by a factor of three [2]. Frontline UK healthcare professionals therefore undergo resuscitation training at intervals between one- and four-yearly, at an estimated annual cost to the NHS of over £17 million [3, 4]. Higher frequency, “low-dose” training up to four times a year has been shown to improve retention of key resuscitation skills among hospital staff by a further factor of three [5] but the resource implications of such a programme using conventional training methods are prohibitive.

Digital simulation has been shown to be highly effective in the acquisition of procedural skills [6] and can be delivered on large scales at low costs, making it an attractive vehicle for high-frequency resuscitation training. However, the paradigm for procedural simulation is underpinned by the identification of an optimal technique for any given intervention, against which a trainee’s performance is evaluated and upon which progression is contingent. Effective resuscitation does not lend itself well to this approach as it pertains largely to decision-making based upon high volumes of dynamic, multi-modal data, the net complexity of which precludes the identifications of a single, optimal course of action. High-fidelity simulation training for the purposes of resuscitation training must thus be nonlinear to account for multiple potential paths of progression towards the desired outcome.

This presents several novel challenges which this project aims to explore. Firstly, software capable of generating high-fidelity, nonlinear simulation must be underpinned by an integrative methodological framework based upon a unified representation scheme of the relevant clinical knowledge base. No such framework exists in this context and it must be developed. Secondly, the large number of unique clinical situations a user might encounter in nonlinear simulation makes it impractical to create conventional performance metrics for each. Metrics by which to objectively evaluate user performance are a prerequisite for facilitating proficiency-based progression, giving constructive feedback and directing future learning, and the question of how one moves beyond incidental learning to apply active educational techniques in the absence of these – and without the need for an expert human presence – must be addressed. Lastly, the efficacy of the end product must be evaluated in a randomised, control trial to test the null hypothesis that such a system will not improve long-term knowledge and skill retention among healthcare professionals undergoing conventional resuscitation training.

Objectives:

1) Investigate the human pre-curated knowledge sources and observational data and identify the unified knowledge representation scheme regarding common resuscitation scenarios.

2) Construct a methodological framework to incorporate and implement both human pre-curated knowledge sources and observational data in an integrative way.

3) Develop a high-fidelity digital clinical simulator upon this framework and incorporate the ability to track trainees’ actions for the purpose of user interaction log analysis.

4) Investigate current pedagogical theory and its application to this system. Consider possible approaches to real-time, objective evaluation of trainee performance in the context of nonlinear simulation.

5) Deliver the completed training system as part of a prospective randomised control trial and measure its effect on knowledge and skill retention among healthcare professionals undergoing conventional resuscitation training.

Anticipated research outcome:

A stand-alone resuscitation training tool that will improve resuscitation knowledge and skill retention among healthcare providers in a cost-effective format and lay the ground for further applications of nonlinear digital clinical simulation within medical education.

Main Supervisor: Dr Ian Menown

Duration: 3

It is not uncommon for patients in Northern Ireland to present suddenly with premature coronary artery disease despite minimal or no conventional risk factors. This project aims to determine the value, over and above conventional cardiovascular risk markers, of emerging lipid markers including lipoprotein(a), markers of inflammatory atherosclerotic plaque and non-invasive vascular imaging including carotid intima media thickness for prediction of premature coronary heart disease. The patient population for study will be identified using regional databases and the recently established familial hyperlipidaemia network.

Main Supervisor: Dr Peter Sharpe

Duration: 3

Development and validation of POCT measurement of lipids [total cholesterol, HDL-C, LDL-C subfractions, Lp(a), Apo B/A] and hsCRP in patients with Familial Hypercholesterolaemia (FH) and correlation with CV risk and events. Also development of new techniques for genotyping. Correlation of all the above with carotid IMT. Development of POCT liver profile testing along with lipids would allow instant clinical decision making at the clinic.

Main Supervisor: Prof Steve Leslie, Dr Mark Grindle

Duration: 3

Atrial fibrillation (AF) is a well-recognised risk factor for stroke. Despite a call for widespread screening, a proportion of patients (~30%) are asymptomatic and remain undiagnosed. Newer mobile and non-mobile technology and cloud based analysis offers the potential for opportunistic screening for AF, but the reliability and acceptability of the technology requires thorough field-tested. This project will undertake to evaluate the potential of new AF detection technologies in the Highlands and Islands with a view to informing discussions about a national screening programme.

Main Supervisor: Dr David Coyle

Main Supervisor: Prof Angus Watson, Dr Sarah-Anne Munoz

Duration: 3

There is a need for well designed, affordable and sustainable housing within the cross border area to address the demands of our social demographic. Technology enabled homes are being built to allow ambient monitoring of home dwellers and the home itself. Monitoring can be combined with digital platforms which will allow home occupants to access and order local services themselves. Smart physical design of these homes will allow them to be adapted for changing care needs, including end of life care. This form of housing may enable early detection and intervention of illness and will facilitate earlier discharge of patients from hospital. The homes will be made available for social rent through housing associations and social enterprise.

Main Supervisor: Prof Ian Megson (PL)

Duration: 3

The project will use existing samples to assess the potential of novel biomarkers for predicting clinical outcome following administration of contrast agents to patients. A range of risk markers will be evaluated with a view to determining suitability of prophylactic measures and of identifying patients at risk before hospital discharge. The project will involve work with clinical samples and laboratory experiments to explore mechanism.

Main Supervisor: Dr Daniel Crabtree, Dr Trish Gorely, Prof Steve Leslie

Duration: 3

.This project will use a mixed method approach to assess the risks and benefits associated with transfer of cardiac rehabilitation into the community in a rural setting. Links to primary care settings and leisure facilities will be amongst the options evaluated and input will be sought from all stakeholders (patients, clinicians, physiotherapists, leisure staff, GPs) in a co-production approach. The use of technology to improve the experience will also be explored.