Cross-domain U-Net framework for unsupervised ECG delineation

Zhiyuan Gao; Ning Wang; Yunjie Chen; Jiaxing Li; Zongmin Wang

doi:10.1117/12.3034158

13 June 2024 Cross-domain U-Net framework for unsupervised ECG delineation

Zhiyuan Gao, Ning Wang, Yunjie Chen, Jiaxing Li, Zongmin Wang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131805V (2024) https://doi.org/10.1117/12.3034158
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

The heartbeat in an electrocardiogram (ECG) serves as the primary basis for diagnosing arrhythmias, consisting of P waves, QRS complexes, and T waves. ECG delineation aims to accurately identify and locate these waveforms, providing support for clinical applications such as the diagnosis of heart diseases. However, due to individual differences among patients or variations in data collection equipment, domain shift often occurs in different datasets, resulting in decreased performance of ECG delineation algorithms. To address this issue, this paper proposes an unsupervised domain adaptation delineation model called DA-Unet. This model first augments the task view using a noise-based data augmentation module and then utilizes a domain adaptation module to narrow the gap between the source and target domain feature spaces, thereby enhancing the model's generalization ability in delineating the target domain. Experimental results demonstrate that this approach effectively improves the cross-dataset performance of the delineation model on two public datasets, QTDB and LUDB.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiyuan Gao, Ning Wang, Yunjie Chen, Jiaxing Li, and Zongmin Wang "Cross-domain U-Net framework for unsupervised ECG delineation", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131805V (13 June 2024); https://doi.org/10.1117/12.3034158

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KEYWORDS

Electrocardiography

Data modeling

Performance modeling

Feature extraction

Signal processing

Cardiovascular disorders

Deep learning

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