Diagnostic Feature Reconstruction for Enhanced Single-Lead ECG Classification

While the standard 12-lead ECG is vital for cardiovascular diagnosis, its reliance on clinical settings hinders daily use. Wearable few-lead devices offer a practical alternative, yet this convenience comes at the cost of diagnostic capability due to reduced lead coverage. To bridge this informational gap and enhance single-lead ECG diagnostic performance, we propose a feature-reconstruction-based classification method for single-lead ECGs. It leverages a pre-trained 12-lead ECG model to extract representative features and guide the feature learning process for single-lead signals. A CNN-Transformer-based multi-scale feature extraction module is introduced for robust ECG feature extraction, followed by a transformer encoder-based reconstruction module to align single-lead features with more discriminative 12-lead representations. A cross-attention based feature fusion module subsequently integrates the reconstructed and original single-lead features to enhance classification performance. By focusing on feature reconstruction rather than signal reconstruction, our method effectively avoids the performance degradation typically caused by signal reconstruction errors and inter-lead redundancy, leading to superior classification outcomes. Evaluation on two public datasets demonstrates that our method enhances feature discriminability and improves single-lead ECG classification performance, confirming its robustness and practical potential.