Analysis of physiological data

I am interested in applying the tools from Statistics and nonlinear dynamics to the analysis of physiological data (especially in the context of Biomedical applications). The advent of new experimental techniques in the field of medicine (like fMRI, NMR, positron tomography, fluorescence microscopy, etc.) has led to the production of very large amounts of data with very high quality. The current challenge is to extract the maximum information from these collections of data in order to improve diagnostics and to assess the best medical treatments.
 

Connection between AP and ECG

In the figure on the right, you see that the integrated ECG actually corresponds to the sum of the electrical activity coming from the different myocytes forming the atria and ventricles. From the point of view of signal analysis, the ECG gives us incomplete information from the electrical status of the heart. To infer the complete "real" state of the heart from the ECG is typically a mathematically "ill-posed" problem and it requires special handling to be solved. In order to get better information from the ECG, I use new algorithms that are beyond the linear tools (Fourier analysis and correlation functions), like Lyapunov spectrum, signal entropy and fractal dimension (see this article from a Master student 2013 about the influence of ageing in the ECG morphology). Note that MD cardiologists are trained to solve this difficult problem of inferring the state of the heart from an examination of the patient's ECG.
 image not available


High resolution voltage map of the left atrium
Atrial fibrillation is the most widespread cardiac disease. It is affecting elderly people. Different treatments are offered to improve the patient's condition. One option consists in ablating the portion of the atrium that is responsible for the onset of fibrillation. This is done with laser surgery or cryotherapy. During the procedure and using the same catheter one can extract a very high resolution map of the lectric state of the atrium (as shown in the right picture). Together with my colleagues we are extracting the maximum statistical information from those maps in order to produce useful biomarkers for quantifying the damage of the atrium. In particular the level of fibrosis is of paramount importance in order to evaluate the long term prognostic of the patient. A summary of this research can be found in 2019. Much more work is needed in order to improve those predictions.
 image not available


For this project I am collaborating with the research group of Dr. Blas Echebarria at UPC (Barcelona) and
Profs. J.I. García-Bolao and J. Díez of the CIMA and CUN (University of Navarra).