A Fast Inter-Patient Domain-Adaptive Arrhythmia Detection Method for Class-Imbalanced ECG Data

Arrhythmia, as a key trigger for sudden cardiac death in cardiovascular diseases, holds significant clinical importance for improving patient outcomes through its early, precise identification and dynamic classification. However, due to factors such as individual variability, differences in recording devices, and variations in recording environments, significant domain shift issues are prevalent across ECG signals from different patients and databases. Additionally, clinical ECG data commonly exhibit highly imbalanced class distributions, severely limiting the generalization performance of existing models across patients and databases. To address these challenges, this paper proposes a lightweight, fast inter-patient domain adaptation framework for arrhythmia recognition tailored to class-imbalanced ECG signals. First, at the feature level, we introduce BLS-FDDA (broad learning system-feature distribution domain adaptation), a domain adaptation method based on the broad learning system (BLS). By leveraging covariance normalization and distribution reconstruction techniques, it thoroughly analyzes the offset between source and target domain features, successfully aligning their feature spaces. By aligning the distributions of the BLS feature expansion matrix, this method avoids reliance on complex models inherent in traditional deep learning frameworks while ensuring effective feature information transfer. Second, at the data level, this paper further proposes a reversible data domain adaptation method, BLS-DRDA (BLS-data reversible domain adaptation). Integrating error perturbation theory, it derives the data transformation relationship between source and target domains. Based on this theoretical derivation, the method achieves rapid adaptation to new data domains without retraining the BLS main model, significantly reducing transfer costs. Moreover, BLS-DRDA preserves the discriminative capability of the original signal during data transformation while effectively preventing information distortion. At the decision layer, addressing the severe class imbalance in arrhythmia data, a cost-sensitive decision algorithm is designed. By introducing concepts of class center distance and sample distribution weights, this algorithm establishes a weighted decision mechanism that effectively mitigates misclassification issues of minority samples during cross-domain transfer. Finally, multi-patient, multidatabase, and continuous domain transfer experiments conducted on the MIT-BIH and INCART public datasets demonstrate that the proposed methods achieve recognition performance approaching 100% in metrics such as accuracy, F1_score, and G_mean, significantly outperforming the original width learning model and various comparative methods. Theoretical analysis and experimental results demonstrate that the proposed BLS-FDDA and BLS-DRDA methods exhibit superior performance across multi-source data, cross-device, and continuous domain adaptation scenarios. This validates the framework’s effectiveness and practicality in complex clinical ECG applications, particularly in the task of identifying arrhythmias across multiple patient categories. The proposed methods substantially enhance recognition capabilities for minority classes and demonstrate exceptional robustness under complex domain shifts and class imbalance challenges.