PhD Study : Detection and Classification of Complications in the Cardiovascular System During Artificial Heart Pump Support

Summary

Ulster University is recognized both nationally and internationally for its outstanding contributions to excellence, innovation, and research. The School of Engineering at Ulster University leads the Centre for Digital Healthcare Technology hub and built close partnerships between healthcare professionals, researchers and industry professionals and it has strong medical technology and digital twin elements. The advertised project is a collaborative initiative involving Ulster University and the Health and Social Care in Northern Ireland. The outcomes of this project will be strongly contribute towards the United Nations Sustainable Development Goal 3: Good Health and Well-being.

Continuous Flow Left Ventricular Assist Devices (CF-LVADs) are miniaturised devices used to support the failing left ventricle in end-stage heart failure patients. Complications, such as aortic and mitral valve dysfunction, left ventricular suction, cardiac arrhythmias, or right ventricular failure during CF-LVAD support, may reduce the effectiveness of CF-LVAD therapy or give rise to further complications in patients. Enhancing these devices for long-term standard care in end-stage heart failure patients involves the crucial improvement of sensing changes in blood flow or pressure within the CF-LVADs.

Currently, there is a lack of long-term reliable flow rate and pressure sensors available for use in CF-LVADs. Consequently, CF-LVAD parameters such as power consumption have been utilised to monitor flow rate. Additionally, research has demonstrated that complications during CF-LVAD support can impact the electrical current signal waveform of CF-LVADs. So, continuous monitoring of CF-LVAD electrical signals and the detection of changes in these signals due to complications in the cardiovascular system will aid in early complication detection. Therefore, it will possible timely intervention before the patient’s condition worsens.

The objective of this project is to develop a method to detect and classify complications in the cardiovascular system during CF-LVAD support by monitoring CF-LVAD intrinsic parameters without relying on external sensors. The project will encompass the modelling of cardiac dynamics for heart failure and CF-LVAD support. Clinical data will be integrated to simulate additional complications, such as aortic and mitral valve dysfunction, left ventricular suction, cardiac arrhythmias, or right ventricular failure, to examine the impact of complications on CF-LVAD electrical signals. Machine learning algorithms will be employed to recognise features in the CF-LVAD electrical signals and detect specific complications.

We are looking for candidates with mechanical, electrical/electronic or biomedical engineering or related backgrounds and strong modelling skills in dynamic systems and machine learning. Knowledge in Matlab and Simulink will be an advantage in the project.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 65%
• Work experience relevant to the proposed project
• Publications - peer-reviewed

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

The University offers the following levels of support:

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living