  Electrocardiography (ECG) is an effective and non-invasive diagnostic tool
that measures the electrical activity of the heart. Interpretation of ECG
signals to detect various abnormalities is a challenging task that requires
expertise. Recently, the use of deep neural networks for ECG classification to
aid medical practitioners has become popular, but their black box nature
hampers clinical implementation. Several saliency-based interpretability
techniques have been proposed, but they only indicate the location of important
features and not the actual features. We present a novel interpretability
technique called qLST, a query-based latent space traversal technique that is
able to provide explanations for any ECG classification model. With qLST, we
train a neural network that learns to traverse in the latent space of a
variational autoencoder trained on a large university hospital dataset with
over 800,000 ECGs annotated for 28 diseases. We demonstrate through experiments
that we can explain different black box classifiers by generating ECGs through
these traversals.
