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We present a novel approach to learn the formulae characterising the emergentbehaviour of a dynamical system from system observations. At a high level, theapproach starts by devising a statistical dynamical model of the system whichoptimally fits the observations. We then propose general optimisationstrategies for selecting high support formulae (under the learnt model of thesystem) either within a discrete set of formulae of bounded complexity, or aparametric family of formulae. We illustrate and apply the methodology on anin-depth case study of characterising cardiac malfunction fromelectro-cardiogram data, where our approach enables us to quantitativelydetermine the diagnostic power of a formula in discriminating between differentcardiac conditions.

Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias