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This paper proposes a domain knowledge-based temporal-spatial graph convolution network for ECG recognition that uses PRQST landmarks and double-stream directed graphs to model intra- and inter-cycle dependencies, achieving state-of-the-art F1 scores on the First Chinese ECG Intelligent Competition dataset.
DeepArrhythmia is a multimodal framework for beat-level ECG arrhythmia classification that combines raw ECG signals and waveform images, using segment-level confidence to selectively acquire physiological evidence for improved accuracy.