Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals

Background: Federated unsupervised learning offers a promising approach to leveraging decentralized data stored on consumer devices, addressing concerns about privacy and lack of annotation. Single-lead electrocardiograms (ECGs) captured on consumer devices are of particular interest due to the global prevalence of cardiovascular disease. The combination of federated and unsupervised learning on biomedical data in a cross-device environment raises questions regarding feasibility and accuracy, especially when considering heterogeneous data. Methods: A randomly selected subset of the Icentia11k open-source dataset containing mobile ECG recordings was used for this study. Heartbeats are labeled as normal, unknown or the pathological classes: premature atrial contraction and premature ventricular contraction. A linear autoencoder model was used as a method to predict the pathological cases using the embedding space and reconstruction error. The model was integrated into a mobile application that supports ECG data recording, preprocessing into heartbeat segments, and participation in a federated learning pipeline as a client node. The autoencoder was trained collaboratively using federated learning with twenty mobile devices, followed by an additional ten epochs of on-device fine-tuning to account for personalization. Results: The approach yielded a sensitivity of 0.87 and a specificity of 0.8 when the predicted anomalies were compared with the ground truth in a binary fashion. Specifically, the detection rate for premature ventricular contraction was excellent with a sensitivity of 0.97. Conclusion: Overall, the approach proved to be feasible in implementation and competitive in accuracy, specifically when the model was fine-tuned to the subject’s data.