Key information

Start date: Between December 2024 and March 2025
Duration: Four years full-time. Stipend available for 3.5 years of the project.
Entry requirements: Applicants must hold a 2:1 undergraduate degree and/or a merit in a Master’s degree.
Eligibility: Open to UK students only

About the project

Sudden cardiac arrest (SCA) accounts for approximately 15-20% of reported deaths in the UK. Those most effected by SCA are people with structural and functional abnormalities of the heart. Scarring of the heart muscle; predominantly from previous heart attacks or other inherited and acquired conditions, form abnormal electrical circuits that can trigger heart rhythms incompatible with life, namely Ventricular Tachycardia (VT). The fundamental basis of this project is to utilise signal processing methods to define the abnormal heart tissue responsible for VT in patients undergoing catheter ablation. Furthermore, the application of machine learning approaches may help with the development of automated methods to locate and eliminate the responsible areas within the heart.

Project summary

VT can be treated by a procedure called ‘catheter ablation’ and this has been shown to significantly reduce VT recurrence rates, reduce hospitalisations and improve survival when compared to medication alone. However, procedural success rates of catheter ablation are modest and the goal is to improve the procedural success rate. The abnormal VT circuits are identified through electro-anatomical mapping, which involves wires being introduced into the heart to examine the electrical properties of the tissue. This routine procedure performed within the NHS generates a vast amount of data from each patient which is projected onto a virtual ‘map’ which is used to guide the ablation procedure. This data is in the form of waveforms known as electrograms (EGMs) which are ideal for signal processing analysis.

An emerging technique to identify important electrical properties in the heart is called ‘decremental evoked potential (DeEP) mapping’. This method highlights EGMs that may be responsible for VT. However, the optimal method to define, analyse and quantify the DeEP EGMs remains unknown.

The aim of this project is to determine the best method of DeEP mapping based on the EGM characteristics. If this can be determined, this can improve procedural outcomes for patients undergoing VT ablation and expand its use within clinical practice.

Methodology

Research aims will be delivered by extracting EGMs from case data obtained from patients undergoing VT ablation at University Hospitals Coventry and Warwickshire (UHCW) NHS Trust. UHCW has an established national and international reputation for expertise in VT ablation and the clinical and research team are focused on optimising mapping methods during VT ablation. EGMs will be characterised using methods such as signal energy, Karhunen-Loeve Transform and eigenfunctions. This will yield optimal discrimination between normal and abnormal tissue through the analysis of large numbers of EGMs. EGM clusters of variables (EGM duration, EGM energy integral of square of signal, number of EGM peaks identified, skew where 50% of the energy is located in the signal, variance of signal derivative) will be translated to the patient procedure map data. This will identify optimal metrics to define the abnormal DeEP substrate. We will finally apply the optimal metrics in a machine learning approach to automate the identification of the diseased heart tissue. This PhD will be ideal for a candidate with a background in mathematics, computer science/engineering or machine learning approaches.

How to apply

You can submit an online application for MPhil/PhD in Medicine via this webpage. If you have any queries please contact wmsrdcoord@warwick.ac.uk.