Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks

Abstract Detection 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 labori...

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Autores principales:	Guillermo Jimenez-Perez, Alejandro Alcaine, Oscar Camara
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3180f946800943f3a9609917b881d0e7
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spelling	oai:doaj.org-article:3180f946800943f3a9609917b881d0e72021-12-02T15:23:02ZDelineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks10.1038/s41598-020-79512-72045-2322https://doaj.org/article/3180f946800943f3a9609917b881d0e72021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79512-7https://doaj.org/toc/2045-2322Abstract Detection 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.Guillermo Jimenez-PerezAlejandro AlcaineOscar CamaraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guillermo Jimenez-Perez Alejandro Alcaine Oscar Camara Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
description	Abstract Detection 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.
format	article
author	Guillermo Jimenez-Perez Alejandro Alcaine Oscar Camara
author_facet	Guillermo Jimenez-Perez Alejandro Alcaine Oscar Camara
author_sort	Guillermo Jimenez-Perez
title	Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
title_short	Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
title_full	Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
title_fullStr	Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
title_full_unstemmed	Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
title_sort	delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3180f946800943f3a9609917b881d0e7
work_keys_str_mv	AT guillermojimenezperez delineationoftheelectrocardiogramwithamixedqualityannotationsdatasetusingconvolutionalneuralnetworks AT alejandroalcaine delineationoftheelectrocardiogramwithamixedqualityannotationsdatasetusingconvolutionalneuralnetworks AT oscarcamara delineationoftheelectrocardiogramwithamixedqualityannotationsdatasetusingconvolutionalneuralnetworks
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Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks

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