Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners

The accurate interpretation of Electrocardiogram (ECG) signals is pivotal for diagnosing cardiovascular diseases. Integrating ECG signals with accompanying textual reports further holds immense potential to enhance clinical diagnostics by combining physiological data and qualitative insights. However, this integration faces significant challenges due to inherent modality disparities and the scarcity of labeled data for robust cross-modal learning. To address these obstacles, we propose D-BETA, a novel framework that pre-trains ECG and text data using a contrastive masked auto-encoder architecture, uniquely combining generative and boosted discriminative capabilities for robust cross-modal representations. This is accomplished through masked modality modeling, specialized loss functions, and an improved negative sampling strategy tailored for cross-modal alignment. Extensive experiments on five public datasets across diverse downstream tasks demonstrate that D-BETA significantly outperforms existing methods, achieving an average AUC improvement of 15% in linear probing with only one percent of training data and 2% in zero-shot performance without requiring training data over state-of-the-art models. These results highlight the effectiveness of D-BETA, underscoring its potential to advance automated clinical diagnostics through multi-modal representations.

What about an ECG signal foundation model?Cardiovascular diseases are the leading cause of death worldwide, accounting for an estimated 17.9 million deaths annually, which is about 32% of all global deaths. Electrocardiograms (ECGs) play a crucial role in diagnosing these conditions, with over 300 million ECGs performed each year globally.Despite the widespread use of ECGs, there's a lack of general-purpose models that can effectively interpret ECG data across diverse populations and conditions. Our work presents D-BETA, a novel approach that learns directly from both ECG signals and their corresponding textual reports simultaneously, without requiring exact manual labels. D-BETA not only captures subtle details in each type of data but also learns how they connect, helping it make a better foundation model with more accurate decisions. Across comprehensive evaluation, D-BETA consistently outperforms strong baselines on 100+ cardiac conditions, offering a scalable, self-supervised path toward accurate, label-efficient heart health AI worldwide.