ECG biomarkers and Arrhythmic risk
The electrocardiographic signal (ECG) has been widely used over the last decades as a noninvasive tool to diagnose many cardiac diseases. The ECG is a realistic and personalized record of the electrical activity of the heart over time. Although different ECG features have been proposed to assess arrhythmic risk, they are not very specific as many of those are present in several disease conditions. The understanding of underlying ionic mechanisms at cellular level that expand to tissue, whole organ and eventually drive physiological ECG changes may improve the search of more specific ECG biomarkers. We are interested in searching novel ECG biomarkers that could prevent the occurrence of life threatening arrhythmias for different disease conditions. Some of the research questions that will focus our attention are:
- How the degree and severity of different pathological ionic changes affect the ECG?
- Which are the expected ECG changes that are driven by abnormalities observed at cellular level and how should we measure them?
- Is the simulated ECG one way of linking cardiac modelling and ECG analysis?
In order to answer those questions we will use a combination of signal processing techniques and cardiac modeling and simulation. We will make use of signal processing techniques to analyze ECG recordings and provide specific biomarkers. Moreover, multiscale modeling and simulation will help us to understand the capability of ECG biomarkers to reflect electrophysiological or structural abnormalities through the computation of the simulated ECG.