Adaptive feature fusion based semi-supervised federated learning system for ECG arrhythmia detection
Abstract
Automatic cardiac arrhythmia detection using electrocardiogram (ECG) signals is essential for early diagnosis of cardiovascular conditions. Most of the previously existing systems for arrhythmia detection depend on fully supervised learning approaches. In addition to that, since the ECG patient data is centrally stored, it can raise concerns like privacy and security in healthcare environments. To address these issues, this work explores a semi-supervised federated learning framework that detects arrhythmia using ECG signals. The MIT-BIH Arrhythmia dataset is used from which the heartbeat segments are extracted and relevant features are obtained. The framework incorporates the use of multi-level feature extraction, adaptive feature fusion and pseudo-labeling to utilize both labeled and unlabeled data efficiently. In order to improve the performance of the model, a weighted federated aggregation approach is used where the contribution of each of the clients is considered during the global model updates. Implementation and evaluation of multiple machine learning and deep learning models including Random Forest, Support Vector Machine, K-Nearest Neighbor, AdaBoost, Gradient Boosting and Multi-Layer Perceptron are performed within the proposed framework. This proposed approach achieves an accuracy of 96.81% in semi-supervised federated settings and has an AUC value of 0.9957 that shows high classification reliability. The system helps improve the model learning with limited…
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