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FedECG: A federated semi-supervised learning framework for electrocardiogram abnormalities prediction
- Source :
- Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 6, Pp 101568- (2023)
- Publication Year :
- 2023
- Publisher :
- Elsevier, 2023.
-
Abstract
- The soaring popularity of smart devices equipped with electrocardiograms (ECG) is driving a nationwide craze for predicting heart abnormalities. Smart ECG monitoring system has achieved significant success by training machine learning models on massive amounts of user data. However, three issues arise accordingly: 1) ECG data collected from various devices may display personal characteristic variations, leading to non-independent and identically distributed (non-i.i.d.) data. These differences can impact the accuracy and reliability of data analysis and interpretation; 2) Most ECG data on smart devices is unlabeled, and data labeling is resource-consuming as it requires heavy-loaded labeling from professionals; 3) While centralizing data for machine learning can address above issues like non-i.i.d. data and labeling difficulties, it may compromise personal privacy. To tackle these three issues, we introduce a novel federated semi-supervised learning (FSSL) framework named FedECG for ECG abnormalities prediction. Specifically, we adopt a pre-processing module to better utilize the ECG data. Next, we devise a novel model based on ResNet-9 in FSSL to accurately predict abnormal signals from ECG recordings. In addition, we incorporate pseudo-labeling and data augmentation techniques to enhance our implemented semi-supervised learning. We also develop a model aggregation algorithm to improve the model convergence performance in federated learning. Finally, we conduct simulations on a real-world dataset. Experiments demonstrate that FedECG obtains 94.8% accuracy with only 50% of the data labeled. FedECG achieved slightly lower accuracy than traditional centralized methods in ECG monitoring, with a 2% reduction. In contrast, FedECG outperforms the state-of-the-art distributed methods by about 3%. Moreover, FedECG can also support unlabeled data and preserve data privacy as well.
Details
- Language :
- English
- ISSN :
- 13191578
- Volume :
- 35
- Issue :
- 6
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of King Saud University: Computer and Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4dd950f5a8834e178bfcac5427cdbbcf
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.jksuci.2023.101568