000165295 001__ 165295
000165295 005__ 20260217205611.0
000165295 0247_ $$2doi$$a10.3389/fcvm.2024.1341786
000165295 0248_ $$2sideral$$a146787
000165295 037__ $$aART-2024-146787
000165295 041__ $$aeng
000165295 100__ $$aJimenez-Perez, Guillermo
000165295 245__ $$aGeneralising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation
000165295 260__ $$c2024
000165295 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165295 5203_ $$aIntroduction: 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.
000165295 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T71-23D$$9info:eu-repo/grantAgreement/ES/MCIU/PID2022-139143OA-I00
000165295 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000165295 590__ $$a2.9$$b2024
000165295 591__ $$aCARDIAC & CARDIOVASCULAR SYSTEMS$$b80 / 231 = 0.346$$c2024$$dQ2$$eT2
000165295 592__ $$a0.975$$b2024
000165295 593__ $$aCardiology and Cardiovascular Medicine$$c2024$$dQ1
000165295 594__ $$a5.5$$b2024
000165295 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165295 700__ $$aAcosta, Juan
000165295 700__ $$0(orcid)0000-0002-0166-2837$$aAlcaine, Alejandro
000165295 700__ $$aCamara, Oscar
000165295 773__ $$g11 (2024), [15 pp.]$$pFront. cardiovasc. med.$$tFrontiers in cardiovascular medicine$$x2297-055X
000165295 787__ $$tData from: QTDB annotations.$$tData from: LUDB annotations.$$tData from: Zhejiang database annotations.$$w10.6084/m9.figshare.14035187.v1$$w10.6084/m9.figshare.14035175.v1$$w10.6084/m9.figshare.14035196.v1
000165295 8564_ $$s16754725$$uhttps://zaguan.unizar.es/record/165295/files/texto_completo.pdf$$yVersión publicada
000165295 8564_ $$s2134629$$uhttps://zaguan.unizar.es/record/165295/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165295 909CO $$ooai:zaguan.unizar.es:165295$$particulos$$pdriver
000165295 951__ $$a2026-02-17-20:49:14
000165295 980__ $$aARTICLE