Description of the thesis topic

PhD student in Graph Signal Processing for the Characterization of Multipolar Electrograms of Persistent Atrial Fibrillation.
Responsible for a significant proportion of brain strokes, atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice. Currently, the most attractive therapeutic option for persistent AF is catheter ablation, where multipolar catheters – i.e., equipped with multiple electrodes – are increasingly used as they facilitate the electroanatomic mapping of the atria before the ablation phase. However, most ablation strategies neglect potentially useful information contained in multipolar atrial electrograms (EGM). In a bid to define a new patient-tailored ablation paradigm, this PhD thesis aims at analyzing the observed multipolar signals while considering the structural configuration (geometry) of the mapping catheter by means of graph signal processing (GSP). The underlying hypothesis is that local propagation patterns in AF are associated with specific space-time signatures in the EGM measured by multipolar catheters, and that such signatures can be captured by suitable GSP tools exploiting the catheter configuration. To test this hypothesis, the thesis will explore graph characterizations of multipolar EGM to derive biomarkers of local propagation patterns and will analyze the ability of the derived biomarkers to identify active drivers sustaining AF. Well-grounded on interpretable mathematical concepts, the GSP approach to be developed in the thesis is expected to yield reproducible, explainable results, increasing the reliability of clinical decision-making.

Activities :
- Study the literature on the themes of the thesis (atrial fibrillation, graph signal processing).
- Propose GSP techniques adapted to the biomedical context of the thesis (multipolar catheters).
- Participate in the generation of synthetic data and the export of real data.
- Evaluate the proposed GSP techniques on synthetic signals and real databases.
- Write reports and scientific publications summarizing the results obtained.
- Present the results orally at team meetings and scientific events (seminars, conferences).
- Possibility of carrying out teaching activities (monitorship).
- Participate in the supervision of trainees up to Master's level.

Skills :
- Mastery of the basic theoretical and algorithmic aspects of signal processing.
- Proficiency in scientific computing tools (preferably MATLAB).
- Experience with biomedical applications of signal processing is desirable.
- Good computing skills.
- Proficiency in word processing tools (Word, LaTeX).
- Ability to work independently and as part of a team.
- Good level of oral and written communication in English.
- Good interpersonal skills.

Experience :
Signal processing: theory, algorithms and biomedical applications

Work Context

The i3S laboratory (https://www.i3s.univ-cotedazur.fr) is a joint research unit between the CNRS and the Université Côte d'Azur with Inria as secondary regulatory authority. i3S was one of the first laboratories to be established in the Sophia Antipolis technology park and brings together about 300 people, including about a hundred lecturers-researchers mainly from 3 components of Université Côte d'Azur: Polytech Nice Sophia, EUR DS4H and IUT Nice Côte d'Azur. The laboratory also brings together 20 researchers from the CNRS and 13 researchers from Inria, not to mention about twenty staff from the technical and administrative teams. Nearly 90 doctoral students, a dozen post-docs, 60 master's or engineering school interns complete the workforce. Attached to the CNRS Institute of Computer Science, its research themes cover a wide spectrum of topics in CNU sections 27 "Computer Science" and 61 "Computer Engineering, Automation and Signal Processing". The laboratory is located at the heart of the Sophia Antipolis technology park, in a dynamic ecosystem that brings together academics and companies of all sizes.

The Signal team of the i3S Laboratory (https://i3s.univ-cotedazur.fr/signal), aims to develop advanced, innovative and adapted tools for the processing of signals or images acquired with biomedical sensor networks (cardiology, neurosciences) or in geosciences (seismology and marine ecology), but also in wireless communications. The team specializes in multi-sensor methods, tensor decompositions and component analysis for the joint processing of multimodal data, notably in the context of invasive (intracardiac electrograms) and non-invasive (surface electrocardiogram) records of cardiac activity for the characterization of arrhythmias.

This interdisciplinary thesis will be carried out in the context of an international consortium composed of specialists in signal processing (i3S Laboratory), computational cardiac modeling (Karlsruhe Institute of Technology, Germany) and interventional cardiology (Nice Pasteur University Hospital and Monaco Princess Grace Hospital). The thesis will be supervised by Vicente Zarzoso (Full Professor, Université Côte d'Azur), coordinator of the collaborative research project GrAF funded by the ANR.

The position is located in a sector under the protection of scientific and technical potential (PPST), and therefore requires, in accordance with the regulations, that your arrival is authorized by the competent authority of the MESR.

Constraints and risks

None