Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large num...

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Autores principales:	Yang Liu, Qince Li, Kuanquan Wang, Jun Liu, Runnan He, Yongfeng Yuan, Henggui Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electrocardiogram multi-label classification deep neural network category correlations category imbalance Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/d25e8d8ab59343f6be14ec1b9c762813
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spelling	oai:doaj.org-article:d25e8d8ab59343f6be14ec1b9c7628132021-11-25T16:55:36ZAutomatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding10.3390/bios111104532079-6374https://doaj.org/article/d25e8d8ab59343f6be14ec1b9c7628132021-11-01T00:00:00Zhttps://www.mdpi.com/2079-6374/11/11/453https://doaj.org/toc/2079-6374Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model–based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.Yang LiuQince LiKuanquan WangJun LiuRunnan HeYongfeng YuanHenggui ZhangMDPI AGarticleelectrocardiogrammulti-label classificationdeep neural networkcategory correlationscategory imbalanceBiotechnologyTP248.13-248.65ENBiosensors, Vol 11, Iss 453, p 453 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electrocardiogram multi-label classification deep neural network category correlations category imbalance Biotechnology TP248.13-248.65
spellingShingle	electrocardiogram multi-label classification deep neural network category correlations category imbalance Biotechnology TP248.13-248.65 Yang Liu Qince Li Kuanquan Wang Jun Liu Runnan He Yongfeng Yuan Henggui Zhang Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
description	Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model–based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.
format	article
author	Yang Liu Qince Li Kuanquan Wang Jun Liu Runnan He Yongfeng Yuan Henggui Zhang
author_facet	Yang Liu Qince Li Kuanquan Wang Jun Liu Runnan He Yongfeng Yuan Henggui Zhang
author_sort	Yang Liu
title	Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
title_short	Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
title_full	Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
title_fullStr	Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
title_full_unstemmed	Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
title_sort	automatic multi-label ecg classification with category imbalance and cost-sensitive thresholding
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d25e8d8ab59343f6be14ec1b9c762813
work_keys_str_mv	AT yangliu automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding AT qinceli automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding AT kuanquanwang automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding AT junliu automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding AT runnanhe automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding AT yongfengyuan automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding AT hengguizhang automaticmultilabelecgclassificationwithcategoryimbalanceandcostsensitivethresholding
_version_	1718412864134316032

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding

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