Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation
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- dc.contributor.author Jimenez-Perez, Guillermo
- dc.contributor.author Acosta, Juan Carlos
- dc.contributor.author Alcaine, Alejandro
- dc.contributor.author Camara, Oscar
- dc.date.accessioned 2025-10-21T05:46:22Z
- dc.date.available 2025-10-21T05:46:22Z
- dc.date.issued 2024
- dc.description.abstract Introduction: Extracting beat-by-beat information from electrocardiograms (ECGs) is crucial for various downstream diagnostic tasks that rely on ECG-based measurements. However, these measurements can be expensive and time-consuming to produce, especially for long-term recordings. Traditional ECG detection and delineation methods, relying on classical signal processing algorithms such as those based on wavelet transforms, produce high-quality delineations but struggle to generalise to diverse ECG patterns. Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. However, supervised ML techniques require large annotated datasets for training, and existing datasets for ECG detection/delineation are limited in size and the range of pathological conditions they represent. Methods: This article addresses this challenge by introducing two key innovations. First, we develop a synthetic data generation scheme that probabilistically constructs unseen ECG traces from “pools” of fundamental segments extracted from existing databases. A set of rules guides the arrangement of these segments into coherent synthetic traces, while expert domain knowledge ensures the realism of the generated traces, increasing the input variability for training the model. Second, we propose two novel segmentation-based loss functions that encourage the accurate prediction of the number of independent ECG structures and promote tighter segmentation boundaries by focusing on a reduced number of samples. Results: The proposed approach achieves remarkable performance, with a F1 - score of 99.38% and delineation errors of 2.19 ± 17.73 ms and 4.45 ± 18.32 ms for ECG segment onsets and offsets across the P, QRS, and T waves. These results, aggregated from three diverse freely available databases (QT, LU, and Zhejiang), surpass current state-of-the-art detection and delineation approaches. Discussion: Notably, the model demonstrated exceptional performance despite variations in lead configurations, sampling frequencies, and represented pathophysiology mechanisms, underscoring its robust generalisation capabilities. Real-world examples, featuring clinical data with various pathologies, illustrate the potential of our approach to streamline ECG analysis across different medical settings, fostered by releasing the codes as open source.en
- dc.description.sponsorship The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Secretariat for Universities and Research of the Government of Catalonia (2017 FI_B 01008). This work was partially funded by Departamento de Ciencia, Universidad y Sociedad del Conocimiento, from the Gobierno de Aragón (Spain) (Research Group T71_23D) and by project PID2022-139143OA-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. The GPU was donated by the NVIDIA Corporation.en
- dc.format.mimetype application/pdf
- dc.identifier.citation Jimenez-Perez G, Acosta J, Alcaine A, Camara O. Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation. Front Cardiovasc Med. 2024 Jul 19;11:1341786. DOI: 10.3389/fcvm.2024.1341786
- dc.identifier.doi http://dx.doi.org/10.3389/fcvm.2024.1341786
- dc.identifier.issn 2297-055X
- dc.identifier.uri http://hdl.handle.net/10230/71597
- dc.language.iso eng
- dc.publisher Frontiers
- dc.relation.ispartof Frontiers in Cardiovascular Medicine. 2024 Jul 19;11:1341786
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2022-139143OA-I00
- dc.rights © 2024 Jimenez-Perez, Acosta, Alcaine and Camara. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Digital healthen
- dc.subject.keyword Electrocardiogramen
- dc.subject.keyword Cconvolutional neural networken
- dc.subject.keyword Artificial intelligenceen
- dc.subject.keyword Delineationen
- dc.subject.keyword Multi-centre studyen
- dc.subject.keyword Data augmentationen
- dc.subject.keyword Segmentationen
- dc.title Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentationen
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion