Key result
A physiological latent moving dipole model more accurately reconstructed missing EKG leads compared to standard probabilistic PCA baselines in datasets with significant missing data.
Why the study?
EKGs contain useful diagnostic information but are typically modeled using generic machine learning tools rather than models that directly represent underlying cardiac electrophysiology and measurement processes.
A physiologically-based generative model of EKGs can more accurately reconstruct missing lead data than standard linear factor models.
May support ECG reconstruction with missing leads; leaves open prospective clinical validation and outcome impact.
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart. EKGs contain useful diagnostic information about patient health that may be absent from other electronic health record (EHR) data. As multi-dimensional waveforms, they could be modeled using generic machine learning tools, such as a linear factor model or a variational autoencoder. We take a different approach:~we specify a model that directly represents the underlying electrophysiology of the heart and the EKG measurement process. We apply our model to two datasets, including a sample of emergency department EKG reports with missing data. We show that our model can more accurately reconstruct missing data (measured by test reconstruction error) than a standard baseline when there is significant missing data. More broadly, this physiological representation of heart function may be useful in a variety of settings, including prediction, causal analysis, and discovery.
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Miller et al. (2018) studied Electrocardiogram (EKG) modeling (n=1,546). Latent moving dipole model vs. Probabilistic PCA (K=3 and K=6) was evaluated on Held-out lead reconstruction error (RMSE). A physiological latent moving dipole model more accurately reconstructed missing EKG leads compared to standard probabilistic PCA baselines in datasets with significant missing data.
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