  This paper formalises the concept of learning symbolic rules from multisource
data in a cardiac monitoring context. Our sources, electrocardiograms and
arterial blood pressure measures, describe cardiac behaviours from different
viewpoints. To learn interpretable rules, we use an Inductive Logic Programming
(ILP) method. We develop an original strategy to cope with the dimensionality
issues caused by using this ILP technique on a rich multisource language. The
results show that our method greatly improves the feasibility and the
efficiency of the process while staying accurate. They also confirm the
benefits of using multiple sources to improve the diagnosis of cardiac
arrhythmias.
