Beschreibung
Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, and one of the main causes of ictus and strokes. Despite the advances in its comprehension, its thorough characterization and the choice of the most suitable therapy are still an open issue. Radiofrequency catheter ablation (CA) is becoming one of the most popular solutions. Yet, very little is known about its impact on heart substrate, thus leading to an inaccurate selection of positive responders to therapy; hence, the need for advanced signal processing tools able to quantify such impact in an objective and quantitative manner. Valuable information about AF can be provided by multilead electrocardiogram (ECG) recordings of heart electrical activity in a noninvasive and cost-effective manner. However, most of standard techniques are affected by several issues, such as their manual computation in only one ECG lead. This work aims at exploiting multilead ECG spatial variability through multivariate decomposition techniques, so as to extract proper predictors of AF termination by CA. This approach would help selecting a patient-tailored therapy, thus increasing CA success rate.
Autorenporträt
Marianna Meo is currently a postdoc researcher at Brigham and Women's Hospital, Harvard Medical School, Boston, USA. She got the Ph.D. at the I3S Laboratory, University of Nice Sophia Antipolis, France, in 2013. Her main research interests include biomedical signal processing, blind source separation and classification applied to cardiology.
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OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
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