000165254 001__ 165254
000165254 005__ 20251219174252.0
000165254 0247_ $$2doi$$a10.1038/s41598-020-79512-7
000165254 0248_ $$2sideral$$a146786
000165254 037__ $$aART-2021-146786
000165254 041__ $$aeng
000165254 100__ $$aJimenez-Perez, Guillermo
000165254 245__ $$aDelineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
000165254 260__ $$c2021
000165254 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165254 5203_ $$aDetection and delineation are key steps for retrieving and structuring information of the electrocardiogram (ECG), being thus crucial for numerous tasks in clinical practice. Digital signal processing (DSP) algorithms are often considered state-of-the-art for this purpose but require laborious rule readaptation for adapting to unseen morphologies. This work explores the adaptation of the the U-Net, a deep learning (DL) network employed for image segmentation, to electrocardiographic data. The model was trained using PhysioNet’s QT database, a small dataset of 105 2-lead ambulatory recordings, while being independently tested for many architectural variations, comprising changes in the model’s capacity (depth, width) and inference strategy (single- and multi-lead) in a fivefold cross-validation manner. This work features several regularization techniques to alleviate data scarcity, such as semi-supervised pre-training with low-quality data labels, performing ECG-based data augmentation and applying in-built model regularizers. The best performing configuration reached precisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and 99.88% for the P, QRS and T waves, respectively, on par with DSP-based approaches. Despite being a data-hungry technique trained on a small dataset, a U-Net based approach demonstrates to be a viable alternative for this task.
000165254 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-20R$$9info:eu-repo/grantAgreement/ES/MINECO/MDM-2015-0502
000165254 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000165254 590__ $$a4.997$$b2021
000165254 591__ $$aMULTIDISCIPLINARY SCIENCES$$b19 / 74 = 0.257$$c2021$$dQ2$$eT1
000165254 592__ $$a1.005$$b2021
000165254 593__ $$aMultidisciplinary$$c2021$$dQ1
000165254 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165254 700__ $$0(orcid)0000-0002-0166-2837$$aAlcaine, Alejandro
000165254 700__ $$aCamara, Oscar
000165254 773__ $$g11, 1 (2021), [11 pp.]$$pSci. rep. (Nat. Publ. Group)$$tScientific reports (Nature Publishing Group)$$x2045-2322
000165254 8564_ $$s1971924$$uhttps://zaguan.unizar.es/record/165254/files/texto_completo.pdf$$yVersión publicada
000165254 8564_ $$s2421316$$uhttps://zaguan.unizar.es/record/165254/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165254 909CO $$ooai:zaguan.unizar.es:165254$$particulos$$pdriver
000165254 951__ $$a2025-12-19-14:43:52
000165254 980__ $$aARTICLE