Feng, Tianlong;
Li, Qingchen;
Zhang, Yuanyuan;
Liao, Yongzhi;
Lu, Di;
Liping, Wang;
Zhao, Jianqin;
... Deng, Jingjing; + view all
(2026)
PathFusion-Net: A Rough Path Theory-Based Deep Learning Model for ECG Arrhythmia Classification.
IEEE Journal of Biomedical and Health Informatics
(In press).
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Abstract
This study introduces a novel electrocardiogram (ECG) arrhythmia classification model, PathFusionNet, which integrates Rough Path Theory with deep learning technologies. The model combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Path Signatures, and Path Development to extract spatial morphological features from ECG images and multi-order temporal representations from ECG signals. By adopting an inter-patient split paradigm, our approach more closely reflects real-world clinical diagnostic settings compared to intra-patient methods. The model demonstrates state-ofthe-art overall classification performance on both the MITBIH Arrhythmia Database and a private clinical dataset, achieving 94.7% and 95.1% accuracy, respectively, under the AAMI four-class standard with an inter-patient split paradigm. On the MIT-BIH dataset, the proposed method attains competitive precision and recall across multiple arrhythmia types, including 95.2%/87.9% for ventricular ectopic beats (V) and 75.7%/92.3% for supraventricular ectopic beats (S), indicating balanced performance across clinically diverse categories. This research highlights the potential of Rough Path Theory in time-series analysis and offers a novel deep learning framework for automated early detection and monitoring of ECG arrhythmias.
| Type: | Article |
|---|---|
| Title: | PathFusion-Net: A Rough Path Theory-Based Deep Learning Model for ECG Arrhythmia Classification |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Rough Path Theory, ECG Arrhythmia, |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216614 |
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