Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition
This paper introduces a domain knowledge-based graph convolution network for ECG recognition, incorporating PRQST landmarks as domain knowledge. A double-stream directed graph models intra- and inter-cycle spatial and temporal dependencies. On the First Chinese ECG Intelligent Competition dataset, the method achieves an overall F1 score of 88.1% and 76.3% for rare categories, outperforming state-of-the-art models.
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[Submitted on 1 Jul 2026]
Title:Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition
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Abstract:In light of strides in Arti cial Intelligence (AI) and its wide spread application, challenges persist in the interpretability of AI models, particularly within specialized domains like healthcare, such as electro cardiograph (ECG) recognition. Rather than relying solely on end-to-end convolutional neural networks, this paper introduces a novel approach using a domain knowledge-based graph convolution network for ECG recognition. Key landmarks points of PRQST, vital to ECG interpreta tion, are incorporated as domain knowledge. The double-stream directed graph is employed to model both intra and inter ECG cycles. Speci cally, spatial directed graphs capture the positional relationships among key points, while temporal directed graphs delineate temporal dependencies between adjacent cycles in extended ECG sequences. Experimental re sults on the First Chinese ECG Intelligent Competition dataset, which speci cally classify ECG into nine categories, prove the e cacy of the proposed model. The overall average F1 score is 88.1%, the average F1 score of rare categories is 76.3%, both outperform the state-of-the-art models. The introduction of domain knowledge did enhance the detec tion performance, especially for rare categories.
Comments: 10 pages, 5 figures. Presented at ICONIP 2024, Auckland, New Zealand. Published in LNCS 15290, Springer, 2025
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
MSC classes: 68T07
Cite as: arXiv:2607.01282 [cs.LG]
(or arXiv:2607.01282v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.01282
arXiv-issued DOI via DataCite
Journal reference: Neural Information Processing (ICONIP 2024), LNCS 15290, Springer, 2025, pp. 92-106
Related DOI:
https://doi.org/10.1007/978-981-96-6588-4_7
DOI(s) linking to related resources
Submission history
From: Zhipeng Zhang [view email] [v1] Wed, 1 Jul 2026 08:41:44 UTC (527 KB)
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