Rare-Beat Detection in Heterogeneous ECG: Federated Learning Under Severe Class Imbalance and Class-Missing Clients

作者：Somayyeh Dehghani, Samuel Pierre · 发表于：IEEE Access · 年份：2026 · DOI：10.1109/access.2026.3715579 · 研究领域：Computer Science

Remote arrhythmia monitoring in Internet of Medical Things (IoMT) systems is challenged by electrocardiogram (ECG) class imbalance, acquisition heterogeneity, and privacy restrictions that limit centralized data sharing. These challenges become more severe in federated learning (FL), where clients may exhibit statistically heterogeneous, non-independent and non-identically distributed (non-IID) data, label skew, and few or no examples of rare arrhythmia classes. This study investigates beat-level arrhythmia classification under heterogeneous and class-missing federated ECG distributions. Beat segments from the MIT–BIH Arrhythmia and St. Petersburg INCART databases were allocated without sample reuse across the original pathology-aware synthetic clients, producing controlled class imbalance and class-missing distributions. Stratified beat-level training, validation, and test subsets were then used to train and evaluate a compact one-dimensional convolutional neural network–bidirectional long short-term memory (1D CNN–BiLSTM) model augmented with a convolutional block attention module (CBAM). To address class imbalance and client heterogeneity, the Synthetic Minority Oversampling Technique (SMOTE) is applied only to local training data and combined with cross-entropy loss, macro-F1-based checkpoint selection, client-specific batch-normalization parameters, and a validation-weighted rare-class-aware aggregation rule. Evaluation is reported using both the native six-class label s...