Cardioformer: Advancing AI in ECG Analysis with Multi-Granularity Patching and ResNet

• URL: http://arxiv.org/abs/2505.05538v1
• Date: Thu, 08 May 2025 16:44:21 GMT
• Title: Cardioformer: Advancing AI in ECG Analysis with Multi-Granularity Patching and ResNet
• Authors: Md Kamrujjaman Mobin, Md Saiful Islam, Sadik Al Barid, Md Masum,
• Abstract summary: Cardioformer is a novel multi-granularity hybrid model.<n>It integrates cross-channel patching, hierarchical residual learning, and a two-stage self-attention mechanism.<n>It consistently outperforms four state-of-the-art baselines.
• Score: 0.6919386619690135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electrocardiogram (ECG) classification is crucial for automated cardiac disease diagnosis, yet existing methods often struggle to capture local morphological details and long-range temporal dependencies simultaneously. To address these challenges, we propose Cardioformer, a novel multi-granularity hybrid model that integrates cross-channel patching, hierarchical residual learning, and a two-stage self-attention mechanism. Cardioformer first encodes multi-scale token embeddings to capture fine-grained local features and global contextual information and then selectively fuses these representations through intra- and inter-granularity self-attention. Extensive evaluations on three benchmark ECG datasets under subject-independent settings demonstrate that model consistently outperforms four state-of-the-art baselines. Our Cardioformer model achieves the AUROC of 96.34$\pm$0.11, 89.99$\pm$0.12, and 95.59$\pm$1.66 in MIMIC-IV, PTB-XL and PTB dataset respectively outperforming PatchTST, Reformer, Transformer, and Medformer models. It also demonstrates strong cross-dataset generalization, achieving 49.18% AUROC on PTB and 68.41% on PTB-XL when trained on MIMIC-IV. These findings underscore the potential of Cardioformer to advance automated ECG analysis, paving the way for more accurate and robust cardiovascular disease diagnosis. We release the source code at https://github.com/KMobin555/Cardioformer.

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