Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias

Understand

We present a novel approach to learn the formulae characterising the emergent behaviour of a dynamical system from system observations.

• At a high level, the approach starts by devising a statistical dynamical model of the system which optimally fits the observations.
• We then propose general optimisation strategies for selecting high support formulae (under the learnt model of the system) either within a discrete set of formulae of bounded complexity, or a parametric family of formulae.
• We illustrate and apply the methodology on an in-depth case study of characterising cardiac malfunction from electro-cardiogram data, where our approach enables us to quantitatively determine the diagnostic power of a formula in discriminating between different cardiac conditions.