Cited by 5ECG Monitoring and AnalysisNon-Invasive Vital Sign MonitoringCardiac electrophysiology and arrhythmias

Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation

Zhang, Yue, Lai, Jiewei, Zhao, Chenyu, Wang, Jinliang, Yan, Yong, Chen, Mingyang, Ji, Lei, Guo, Jun +5 more·2024·Artificial Intelligence in Medicine

Abstract

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous pathological ECG patterns, not only requiring the recognition of normal ECG but also addressing an extensive range of abnormal ECG patterns, posing a challenging task. Therefore, we propose an abnormal recognition-assisted network to integrate supplementary information on diverse ECG patterns. Simultaneously, we design an onset-offset aware loss to enhance precise waveform localization. Specifically, we establish a two-branch framework where ECG delineation serves as the target task, producing the final segmentation results. Additionally, the abnormal recognition-assisted network serves as an auxiliary task, extracting multi-label pathological information from ECGs. This joint learning approach establishes crucial correlations between ECG delineation and associated ECG abnormalities. The correlations enable the model to demonstrate sufficient generalization in the presence of diverse abnormal ECG patterns. Besides, onset-offset aware loss focuses intensively on wave onsets and offsets by applying biased weights to various waveform positions. This approach ensures a focus on precise localization, facilitating seamless integration into cross-entropy loss function. A large-scale wearable 12-lead dataset containing 4,913 signals is collected, offering an extensive range of ECG data for model training. Results demonstrate that our method achieves outstanding performance on two test datasets, attaining sensitivity of 94.97% and 94.27% and an error tolerance lower than 20 ms. Furthermore, our method is effective for various aberrant ECG signals, including ST-segment changes, atrial premature beats, and right and left bundle branch blocks.

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Crossref · 10.1016/j.artmed.2024.102992

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Authors
Zhang, Y., Lai, J., Zhao, C., Wang, J., Yan, Y., Chen, M., Ji, L., Guo, J., Han, B., Shi, Y., Chen, Y., Yang, W., & Feng, Q.
Affiliations
  • Southern Medical University (CN)
  • Chinese PLA General Hospital (CN)
Corresponding author
  • Chen, Mingyang
  • Chen, Yundai
  • Yang, Wei
  • Feng, Qianjin
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2024-11
Volume
157
Pages
102992
ISSN
0933-3657 (ISSN-L)
OpenAlex
W4402981648
Cited by
5
References
46
Citations by year
2026: 2 · 2025: 3
FWCI
1.50
Type
journal article
Language
en
Open access
closed
Locations
  • Artificial Intelligence in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords
ECG delineation, wearable ECG, multi-branch network, joint learning, arrhythmia detection, bundle branch block, premature atrial contractions, ST-segment changes, 12-lead ECG, remote cardiac monitoring
MeSH terms
Wearable Electronic Devices, Algorithms, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Neural Networks, Computer

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