MSc Internship Proposal 2016-2017
“Compressed sensing of atrial fibrillation”
Context: The internship will take place at the I3S Laboratory, in the context of an ongoing interdisciplinary collaboration with the Cardiology Department of Monaco “Princess Grace” Hospital.
Topic description: Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice, especially affecting the elderly. Held responsible of up to 25% of strokes, this cardiac condition is considered as the last great frontier of cardiac electrophysiology as it continues to puzzle cardiologists. AF can be analyzed noninvasively in the surface electrocardiogram (ECG). Spectral features of the atrial activity signal present in the ECG provide useful information about the cardiac condition, as they correlate with intracardiac measures and therapy outcome. However, computing such features requires the cancellation of the ventricular artifact (QRST complex) that masks the atrial signal at each heartbeat occurrence .
A major breakthrough in signal processing and data analysis, compressed sensing (CS) allows the recovery of signals sampled under the Nyquist rate when they accept a sparse representation in certain domain . The present MSc internship project will explore the recovery and analysis of atrial activity signals in AF using the CS paradigm. In the first place, existing algorithms for signal reconstruction based on combined L1-L2 norms will be implemented. The tested algorithms will then be validated on realistic synthetic AF signals as well as real data acquired from AF patients. The main goal will be to determine the minimum duration of observed atrial activity signal allowing its accurate recovery by means of CS techniques in a variety of clinical scenarios.
If successful, the internship could be prolonged into a PhD thesis.
Pre-requisites: Candidates will require a strong background in mathematics, statistics and signal processing. Previous experience with biomedical signals, in particular the ECG, and familiarity with cardiac electrophysiology will also be desirable.
Contact: Vicente Zarzoso, I3S Laboratory, University of Nice Sophia Antipolis (email@example.com).
 V. Zarzoso, O. Meste, P. Comon, D. G. Latcu and N. Saoudi, "Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques", in: F. Cazals and P. Kornprobst (Eds.), Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor, Berlin, Heidelberg: Springer Verlag, 2013, chapter 3, pp. 83-116.
 E. J. Candès and M. B. Wakin, "An introduction to compressive sampling", IEEE Signal Processing Magazine, Vol. 25, No. 2, pp. 21-30, March 2008.
(c) GdR 720 ISIS - CNRS - 2011-2015.