Thesis
Please check our research page for more info on our current research
Our research group
We are a young and dynamic research group at the department of Physics and Astronomy led by Prof. Vandersickel (nele.vandersickel@ugent.be). We perform research at the edge of science including physics, computer programming and medicine, a truly interdisciplinary setting. Our research focuses on the analysis of cardiac arrhythmia, which is the main cause of death in the Western society. We provide weekly guidance, step by step, while we will also invite you to come up with own ideas and allow room for creativity. We will give you insight in the scientific process of solving problems, while also focusing on personal growth. We aim for the thesis to allow you to do interesting research while also enjoying the process. Ask a former master student how they experienced their master thesis with us.
What are we looking for? We do not care about your previous scores, but we ask a motivated mindset and a love for programming and solving problems.
What will you do? We do not make subjects just to keep you busy, but you will actually contribute to our scientific research. We have selected subjects which are possible to investigate during one year, while also being able to actually make a contribution. If you succeed, your research should be able (maybe joined with other research) to be published in a scientific journal. How cool would that be?
2024-25 thesis subjects
We present some subjects which seem relevant to us. If you have a specific interest, please also talk to us, and we might be able think of other subjects which match your interests. We can also bring you to the clinic so you can observe ablation procedures and explain and discuss your results with the medical doctors.
DGM stands for Directed Graph Mapping, a software package to analyze cardiac arrhythmias developed by our research group
Enhancing the transition from simulated to clinical data in cardiac arrhythmia
Currently many methods we develop show promising results for simulated data but fail to do so on clinical data. Possible underlying reasons are the sparse and noise polluted clinical data that fail to produce good quality graphs. However, the definition of good quality graphs is still unclear. To determine graph statistics that correlate with graph quality, one strategy is to analyse what the differences are between graphs originating from simulated and clinical data. By systematically polluting simulated data and studying the evolution of graph statistics as a function of pollution magnitude, interesting results and insights can be obtained as to why certain graph-theory measures do or do not produce valid results for clinical data. Using these results, recommended values for graph statistics that correlate with good quality graphs can be proposed.
Some examples of methods to introduce complexity to simulated data include:
- phase randomization of signals (https://doi.org/10.1016/0167-2789(92)90102-S)
- removing data points (https://doi.org/10.3389/fphys.2021.782176)
- removing edges
- applying temporal gaussian white noise to the signals (https://doi.org/10.1016/j.compbiomed.2024.108138)
- introducing fibrosis/scar tissue patterns (https://doi.org/10.21203/rs.3.rs-254560/v1)
- introducing an autoregressive clinical noise model (https://doi.org/10.21203/rs.3.rs-254560/v1)
Some examples of graph metrics include: statistics (median, mean, variance, ...) of the following distributions:
- Nodes: LATs, in- and/or out-degree, local spatial density
- Edges: spatial distances, DeltaLAT, CV, angles, helmholtz decomposition component magnitudes
- Triangles: surface area, angles, interpolated LATs
Investigating Conduction Velocity Patterns in Relation to Scar Tissue in cardiac arrhyhmia
In this project, we aim to enhance DGM software by incorporating the crucial capability to display conduction velocities for cardiac arrhythmia datasets. To ensure robustness, we will commence with simulation studies to establish benchmarks, implementing various approaches outlined in existing literature. Subsequently, we will analyze experimental datasets to evaluate the performance of these implementations and discern the optimal method. Additionally, we will explore the correlation between conduction velocities and scar tissue, providing valuable insights into arrhythmia mechanisms and potential therapeutic avenues.
Scar and anatomical obstacle identification to improve ablation therapy of atrial tachycardia
Knowledge of areas of anatomical obstacles and scar tissue is important for the functioning of DGM. Currently we depend on a combination of annotations done automatically by the data-collection system, and manual annotations. Your task will be to use machine learning models, taking as features signal and geometrical properties, in order to identify the areas of interest.
Unlocking Cardiac Arrhythmia Insights: Enhancing DGM Software for Advanced Signal Analysis
Currently, we just locate the LAT on the signal of the local electrograms, which just gives us a single information punt from a whole signal. Last year, a engineering student also implemented how to track double potentials. This is extremely useful for the EP to better understand the cardiac arrhythmia as it shows regions of conduction block. However, this year, you will take it one step further and also implement the signals that have fractionation. There are over 27 different definitions for a fractionated signal. In short, fractionated signals indicate slower conduction velocity of the signal, and thus diseased heart, which become interesting abaltion targets. You will investigate these different definitions and implement the ones that seem the best. We will have access to a great new dataset with amazing signals, so this will be extremely interesting to add this feature to DGM.
Venturing Beyond the Beat: Probing the Behavior of Rotors Across a Spectrum of Cardiac Cell Models
Rotors are fundamental to many cardiac arrhythmias, yet our understanding of their behavior across diverse cell models remains fragmented. While computer simulations offer a window into these phenomena, the multitude of available atrial and ventricular cell models presents a challenge in gaining a comprehensive overview. We propose a thorough examination and comparison of rotor behavior across all accessible cell models in openCARP. Leveraging the capabilities of DGM software, we aim to elucidate how spiral waves and rotors manifest in these varied cellular environments. While a student embarked on this project last year, numerous unexplored avenues beckon for further investigation.
Deciphering Spiral Wave Drift in Cardiac Dynamics
Spiral waves are pervasive in excitable media, manifesting in phenomena ranging from chemical reactions to brain tissue and the heart. Their emergence can intricately shape spatial dynamics, yielding both favorable and deleterious outcomes. Of particular concern is their role in precipitating life-threatening cardiac arrhythmias, with ventricular fibrillation standing as a leading cause of mortality in the industrialized world.
Within the realm of biophysics, the study of spiral waves occupies a significant domain, owing to their classification as nonlinear waves and self-organizing phenomena. Employing a fusion of biophysical and computational methodologies, researchers endeavor to unravel the complexities inherent in these waves. Investigations span from direct clinical applications and patient data analysis to fundamental inquiries aimed at elucidating universal principles governing spiral wave dynamics.
Within our biophysics group, we offer a diverse array of master projects catering to those interested in immediate clinical relevance. However, we also present an opportunity for exploration into the fundamental aspects of spiral wave dynamics, delineated below.
A central enigma within the theory of spiral waves lies in comprehending the mechanisms governing their drift. Typically stable or exhibiting minor meandering, spiral waves can undergo spatial displacement, a phenomenon with profound implications for cardiac arrhythmogenesis. The direction of drift, often dictated by the heterogeneity of cardiac tissue, significantly influences the trajectory of arrhythmias. While early numerical simulations suggested a tendency for drift towards regions of longer wave period, the underlying mechanism remains elusive, prompting an urgent need for comprehensive investigation.
Recent advancements in numerical tools have revolutionized the study of spiral waves, facilitating simultaneous exploration across multiple cardiac cell models. Analogous to weather forecasting, where diverse models are leveraged to predict atmospheric dynamics, this project aims to undertake a comprehensive inquiry into spiral wave drift within the heart. Specifically, we seek to discern whether drift consistently aligns with longer periods, or if alternate dynamics prevail. Through rigorous analysis, we aspire to uncover the fundamental laws governing spiral wave drift and potentially propose mechanisms dictating its directionality.
This endeavor promises not only to unravel a longstanding mystery in the realm of nonlinear wave theory but also holds profound clinical implications. With clear objectives and leveraging novel methodologies, this project offers an opportunity for students to contribute to cutting-edge research, with the potential for impactful publications.