Transfer learning for ECG classification

Abstract Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that...

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Autores principales:	Kuba Weimann, Tim O. F. Conrad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df807c03d73744959547ff9456ab9316
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spelling	oai:doaj.org-article:df807c03d73744959547ff9456ab93162021-12-02T13:19:21ZTransfer learning for ECG classification10.1038/s41598-021-84374-82045-2322https://doaj.org/article/df807c03d73744959547ff9456ab93162021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84374-8https://doaj.org/toc/2045-2322Abstract Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to $$6.57\%$$ 6.57 % , effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning .Kuba WeimannTim O. F. ConradNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kuba Weimann Tim O. F. Conrad Transfer learning for ECG classification
description	Abstract Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to $$6.57\%$$ 6.57 % , effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning .
format	article
author	Kuba Weimann Tim O. F. Conrad
author_facet	Kuba Weimann Tim O. F. Conrad
author_sort	Kuba Weimann
title	Transfer learning for ECG classification
title_short	Transfer learning for ECG classification
title_full	Transfer learning for ECG classification
title_fullStr	Transfer learning for ECG classification
title_full_unstemmed	Transfer learning for ECG classification
title_sort	transfer learning for ecg classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/df807c03d73744959547ff9456ab9316
work_keys_str_mv	AT kubaweimann transferlearningforecgclassification AT timofconrad transferlearningforecgclassification
_version_	1718393301186379776

Transfer learning for ECG classification

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