Abstract
Objective
Upper-limb electrocardiogram (ECG) acquired from a single-arm wearable device offers a practical approach for dynamic cardiac monitoring. This study addresses whether single-channel Arm-ECG, characterized by higher noise and distinct morphology compared to standard 12-lead ECG, can reliably detect arrhythmias.Methods
We propose CLMF-Net, a contrastive multitask framework featuring multiscale convolutional layers to capture temporal-morphological patterns. A fine-grained reconstruction branch preserves subtle clinical features, including P waves and ST segments, while a contrastive module ensures robustness against signal quality variations. The complete framework is trained end-to-end using a unified loss function that balances classification, reconstruction, and consistency objectives.Results
Validation with 132 elderly participants undergoing six-minute walk tests confirmed the feasibility of dynamic Arm-ECG acquisition. CLMF-Net, trained for arrhythmia identification from single-channel ECG, achieved 95.19% accuracy on the Arm-ECG dataset. It effectively detects arrhythmias, despite challenges with noise and morphology. Generalizability was evidenced by 95.55% and 84.23% accuracy on the Chapman and China Physiological Signal Challenge 2018 (CPSC2018) datasets, respectively, demonstrating the model's ability to classify multiple arrhythmia types across diverse datasets.Conclusion
Beyond precise identification of arrhythmias, deep feature and morphology analyses showed that the learned representations do not always align with clinically emphasized intervals. This discrepancy highlights both the promise of leveraging Arm-ECG signals and the caution required when translating model-derived interpretations into clinical workflows.Significance
This study validates the clinical utility of single-arm ECG monitoring, demonstrating feature reliability for real-world cardiac health assessment and supporting the translational potential through consistency with standard recordings.