Kapsecker Maximilian, Jonas Stephan M
TUM School of Computation, Information and Technology, Technical University of Munich, Garching bei München, Bavaria, Germany.
Institute for Digital Medicine, University Hospital Bonn, Bonn, North Rhine-Westphalia, Germany.
PLOS Digit Health. 2025 Apr 7;4(4):e0000793. doi: 10.1371/journal.pdig.0000793. eCollection 2025 Apr.
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.
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.
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.
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.
联邦无监督学习提供了一种很有前景的方法，可用于利用存储在消费设备上的分散数据，解决对隐私和缺乏标注的担忧。由于心血管疾病在全球的普遍存在，消费设备上捕获的单导联心电图（ECG）尤其令人关注。在跨设备环境中对生物医学数据进行联邦学习和无监督学习的结合，引发了关于可行性和准确性的问题，特别是在考虑异构数据时。
本研究使用了Icentia11k开源数据集中随机选择的一个子集，该子集包含移动ECG记录。心跳被标记为正常、未知或病理类别：房性早搏和室性早搏。线性自动编码器模型被用作一种方法，利用嵌入空间和重建误差来预测病理情况。该模型被集成到一个移动应用程序中，该应用程序支持ECG数据记录、预处理为心跳段，并作为客户端节点参与联邦学习管道。自动编码器使用联邦学习在20台移动设备上进行协同训练，随后进行另外10个轮次的设备上微调以实现个性化。
当以二元方式将预测的异常与真实情况进行比较时，该方法的灵敏度为0.87，特异性为0.8。具体而言，室性早搏的检测率极佳，灵敏度为0.97。
总体而言，该方法在实施中被证明是可行的，在准确性方面具有竞争力，特别是当模型针对受试者的数据进行微调时。
