Heart Block Identification from 12-Lead ECG: Exploring the Generalizability of Self-Supervised AI

Timely diagnosis and treatment of heart blocks are critical for preventing fatal outcomes in patients with cardiac conduction disorders. Expert analysis of clinical 12-lead electro-cardiograms (ECG) remains the standard diagnosis apparatus. In this study, we propose a self-supervised deep learning model that can detect evidence of heart blocks in ECG signals. We build a multichannel residual neural network (KRes) and train this model in a self-supervised contrastive fashion leveraging 3.6 million ECG from patients at the Massachusetts General Hospital (MGH) to learn robust ECG representations. We evaluate the utility of such representations on a large public dataset, PTB-XL, through fine-tuning and linear probing for identifying heart blocks. To compare, we also build a baseline supervised model by adapting KRes. The training data contains a subset of 10.6 thou-sand PTB-XL ECG from patients with and without heart block patterns, and a holdout set of 1319 ECG is used for evaluation. We compare the performances of the finetuning and the linear probing of the self-supervised representations with that of the supervised model using the area under the receiver-operating curve (AUC), sensitivity, specificity, and predictive values, and found those performing equally well. Moreover, we repeat the training cycles of the three pipelines while reducing the number of training samples and demonstrate that the performance of the self-supervised representations remain steady even when labeled data is limited. This research underscores the potential of self-supervised learning in cardiac diagnostics, emphasizing generalizability and performance across diverse datasets and clinical settings, especially in data-scarce paradigms.