Joint Detection of Rhythmic and Morphological Abnormalities in Electrocardiographic Images: A Multitask Learning Approach

The electrocardiogram (ECG) is the most widely used diagnostic tool for the characterization of heart function. Although automated methods of ECG interpretation can improve clinical care, but most methods are designed on signal-based data. In this work, we consider images of paper-based representations of multichannel ECG to develop intelligent methods for its analysis. Cardiovascular abnormalities are manifested in ECG through either morphological alterations, rhythmic variations, or a combination of both. To effectively classify these cardiac abnormalities, we formulate a multitask learning framework comprising two primary tasks relating to the classification of morphological and rhythmic abnormalities and an auxiliary task on delineating regions pertaining to the primary tasks. We employ a dynamic task weighting approach based on homoscedastic uncertainty to balance the task-specific losses in the multitask framework. We evaluate our method on two databases: an internal database containing clinical ECG images obtained from multiple medical centres in Assam, India, and the other comprising ECG images extracted from a publicly available 12-lead ECG dataset. Experimental evaluation shows that our proposed deep architecture outperforms single-task learning counterparts and achieves promising performance for both morphological ailments and rhythm classification tasks. Results also demonstrate superior performance compared to other image-based state-of-the-art methods. Moreover, analysis of the post-hoc interpretation in the form of saliency maps verifies the model's performance and provides clinically meaningful inferences to its predictions.