Time-Varying Topological Descriptors for Cardiac Disease Diagnosis

Abstract

Cardiac diseases are among the most common illnesses in the world, and data scientists have created a wide range of tools to contribute to their detection and diagnosis. In particular, topological data analysis has been used to work with medical imaging and specifically with cardiac magnetic resonance images. This project introduces the use of time-varying topological descriptors along a cardiac cycle and applies them for disease diagnosis. The methods used aim to develop the relationship between topological data analysis and temporal data. We also intend to contribute to the simplification, interpretability and improvement of a computational approach to cardiac disease diagnosis, which usually involves costly calculations of radiomics or potential black boxes.