This project is funded by:
Atrial fibrillation (AF) and heart failure (HF) are often manifest as coexistent conditions increasingly recognised as global cardiovascular epidemics over the last decade and expected to increase in prevalence over the coming years. Cardiac rhythm monitoring is important for the early diagnosis of these cardiac conditions. Furthermore, non-invasive, long-term cardiac output (CO) and left-ventricular ejection time (LVET) measurements from impedance cardiography (ICG) signal recording is currently not available. An implantable-loop-recorder (ILR) would accurately monitor cardiac rhythm for extended periods, but they cannot monitor CO; additionally, they require a surgical procedure. The simultaneous integration of long-term non-invasive ECG and ICG signal recordings and real-time arrhythmia detection, would be a significant advance on current cardiac diagnostic methodologies and enabling technologies. A wearable cardiac monitoring armband device would offer advantages for long-term continuous recordings of cardiac vital signals (ECG and ICG): no adhesives, no irritating electrode gels, no connecting cables and comfortable to wear. Thus, automatic ECG-based detection/classification of AF in addition to ICG sensing from the arm location will be evaluated as non-conventional alternative methods. Reproducibility agreement assessed against conventional sensing methods on the chest. A clinical knowledge database from patient armband ECG and ICG recordings gathered from the Craigavon Area Hospital (Northern Ireland) will be made available, allowing production of linear regression models to predict conventional (chest based sensors) ICG hemodynamic measurements.
AF and HF armband methods and technology will have multiple applications, addressing significant unmet needs in clinical cardiology. It will be useful in the diagnosis of palpitations, dizzy episodes and HF signs of common presentations in general practice. Detection of atrial fibrillation will allow early treatment with reduction in stroke risk.
A PhD researcher for this project having a major background knowledge in the fields of Biomedical Engineering and/or Electronic Engineering, with additional expertise in digital-signal-processing, would be advantageous.
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.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
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.
This project is funded by:
These scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance). A Research Training Support Grant (RTSG) of £900 per annum is also available.
To be eligible for these scholarships, applicants must meet the following criteria:
Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.
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.
Submission deadline
Monday 24 February 2025
04:00PM
Interview Date
Mach 2025
Preferred student start date
15th September 2025
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