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 glob...

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Veröffentlicht in:PLOS digital health Jg. 4; H. 4; S. e0000793
Hauptverfasser: Kapsecker, Maximilian, Jonas, Stephan M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.04.2025
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Cardiac arrhythmia
Cardiovascular diseases
Communication
Computer and Information Sciences
Customization
Datasets
Electrocardiography
Energy consumption
Engineering and Technology
Investigations
Machine learning
Medicine and Health Sciences
Physical Sciences
Privacy
Research and Analysis Methods
Smartphones
Wearable computers
ISSN:2767-3170, 2767-3170
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Zusammenfassung: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.
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The authors have declared that no competing interests exist.
ISSN:2767-3170
2767-3170
DOI:10.1371/journal.pdig.0000793