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• CG-DMER employs spatial-temporal masked modeling to capture fine-grained temporal dynamics and inter-lead spatial dependencies in ECG signals.method 70%
• CG-DMER utilizes a representation disentanglement and alignment strategy to mitigate modality-specific biases by using modality-specific and modality-shared encoders.method 70%
• CG-DMER achieves state-of-the-art performance across diverse downstream tasks on three public ECG datasets.result 70%
• The CG-DMER framework can outperform traditional ECG interpretation software and multimodal systems that do not explicitly handle modality noise.result 70%
• CG-DMER was tested on ECG datasets including PTB-XL, CPSC2018, and CSN.method 70%
• The approach demands significant computational resources for training.limitation 70%
• Integration into current clinical workflows may be challenging due to the need for both ECG and detailed textual data alignment.limitation 70%
• The market for cardiovascular diagnostics is vast and includes potential clients such as healthcare providers, telemedicine services, and electronic health record companies.market 70%

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