# Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition

arXiv: https://arxiv.org/abs/2607.01282 · HTML: https://mlsanity.com/paper/2607.01282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [Skeleton-based Action Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1704.07595/) (2017)
- [Understanding the Mechanisms of Deep Transfer Learning for Medical Images](https://mlsanity.com/paper/1704.06040/) (2017)
- [Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment](https://mlsanity.com/paper/2105.10709/) (2021)
- [Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention](https://mlsanity.com/paper/1908.10088/) (2019)
- [Sensing as a Service Model for Smart Cities Supported by Internet of Things](https://mlsanity.com/paper/1307.8198/) (2013)
- [Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks](https://mlsanity.com/paper/1908.06802/) (2019)

## Abstract (arXiv)

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.