Post-Hoc Uncertainty Quantification for QT Interval Measurements with Ensembles of Electrocardiographic Leads and Deep Models

Mously Diaw, Stéphane Papelier, Alexandre Durand-Salmon, Jacques Felblinger, Julien Oster

Keywords: uncertainty quantification, deep learning, Bayesian, conformal prediction, electrocardiogram

Abstract: Standard electrocardiography (ECG) allows to record the electrical activity of the heart from different angles called leads. The QT interval, which corresponds to the time elapsed between the onset of ventricular contraction and the end of ventricular relaxation, is an ECG biomarker of drug cardiotoxicity. Deep neural networks (DNNs) have achieved state-of-the-art performance in QT interval measurement but are missing uncertainty quantification, which is necessary for safer decision making. Uncertainty is usually encoded in DNNs through probability distributions over model weights. In this paper, we combine this approach with notions of multisensory integration whereby neural systems account for uncertainty by optimally integrating all available sensory inputs. We thus approximate the posterior predictive distribution of the QT interval given a multi-lead ECG as a weighted average across leads (lead integration) and models (deep ensembling) and derive 100(1 − α)% Bayesian prediction intervals (PIs). We apply this method to QT-based cardiac drug safety monitoring and compare it to an adapted version of conformal prediction. The Bayesian and conformal approaches yield comparable empirical coverage (77%-82% for mean PI widths of ∼28 milliseconds, α = 0.1). The former is more straightforward and shows better error-based calibration. Data and code implementation are available at https://github.com/mouslyddiaw/qt-uncertainty.

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