Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias
This work addresses the challenge of diagnosing cardiac malfunctions from ECG data, but it appears incremental as it builds on existing methods for learning formulae from dynamical systems.
The authors tackled the problem of learning temporal logical properties from observations of dynamical systems, specifically to discriminate between cardiac arrhythmias using ECG data, and demonstrated the approach's ability to quantitatively determine the diagnostic power of formulae in distinguishing different cardiac conditions.
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.