Seminar: Introduction to Signal Processing in Atrial Fibrillation Analysis

The seminar will be given by Dr. Luigi Yuri Di Marco, University of Sheffield, UK, as part of the course on "Elaborazione di Dati e Segnali Biomedici M".

  • Date: 13 May 2015 from 16:00 to 18:00

  • Event location: Room 5.5, School of Engineering and Architecture, viale Risorgimento 2, Bologna

Contact Name:

Contact Phone: +39 051 209 3095


Atrial fibrillation (AF) is the most common form of sustained arrhythmia in clinical practice, a major cause of morbidity affecting 1-2% of the population above 50 years of age. Catheter ablation (CA) is increasingly becoming the treatment of choice for persistent AF, especially for middle-aged patients who are refractory to drug treatment. However, not all patients respond to CA. A pre-procedural screening criterion to identify those who will likely benefit from this surgical treatment is of paramount clinical interest. To this end, a substantial research effort has been devoted in the past two decades to the analysis of intracardiac electrograms and body surface potentials, with the goal of characterizing AF substrate and ultimately predicting CA acute and postoperative outcomes. This seminar provides an overview of the most widely adopted techniques in the quantitative analysis of intracardiac and surface recordings of AF, highlighting open challenges. The illustration of methods is supported by clinical data collected in the catheterisation laboratory, and by data from public databases (Physionet).

About the speaker

Luigi Yuri Di Marco received the PhD degree in Bioengineering from the University of Bologna in 2012. During his PhD studies, he worked at the Institute of Cellular Medicine of Newcastle University, UK, for a visiting period of 6 months. He later rejoined the group for one year. As of March 2013, he is with the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, UK. His research interests include cardiovascular physiology; cardiac arrhythmia; vascular cell biology and mathematical modeling; biomedical signal processing; machine learning in supervised classification.