Search papers, labs, and topics across Lattice.
This study introduces Graph-CMMC, a novel graph-based pseudo-multimodal contrastive learning framework designed to enhance the analysis of 12-lead ECG signals by addressing the limitations of existing methods that often treat leads independently. By transforming ECG waveforms into Gramian Angular Difference Field (GADF) images, the framework captures both local and global waveform patterns while modeling inter-lead dependencies through a graph-based relational module. Experimental results reveal that Graph-CMMC outperforms traditional supervised learning approaches in classifying coronary artery occlusions, highlighting the potential of GADF representations and structured learning in ECG analysis.
Transforming ECG signals into GADF images reveals critical inter-lead dependencies, leading to superior classification performance in coronary artery disease detection.
12-lead electrocardiogram (ECG) is a standard, non-invasive examination widely used for diagnosing coronary artery disease, where clinical interpretation relies on comparing waveform patterns across multiple leads. However, most existing ECG analysis methods focus on single-lead signals or treat each lead independently, and typically process ECG signals as one-dimensional time-series data using CNNs or RNNs. While effective in modeling local waveform changes, such approaches have difficulty capturing inter-lead dependency and global waveform patterns essential for clinical diagnosis. To address this limitation, we propose a graph-based pseudo-multimodal contrastive learning framework called Graph-CMMC. ECG waveforms are transformed into Gramian Angular Difference Field (GADF) images to construct complementary representations of the same cardiac activity, enabling a pseudo-multimodal learning setting. Using all 12 leads, Graph-CMMC aligns waveform and GADF representations in a self-supervised manner, while a graph-based relational module is employed to model inter-lead dependency and enforce structural consistency across leads during contrastive learning. Experimental results on a multi-label coronary artery occlusion classification task demonstrate that the proposed framework achieves competitive performance compared to supervised learning methods. These results further suggest the effectiveness of using GADF as a complementary representation and incorporating explicit graph-based modeling of inter-lead dependency for learning robust 12-lead ECG representations.
