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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615597/ https://www.ncbi.nlm.nih.gov/pubmed/34821669 http://dx.doi.org/10.3390/bios11110453 |
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author | Liu, Yang Li, Qince Wang, Kuanquan Liu, Jun He, Runnan Yuan, Yongfeng Zhang, Henggui |
author_facet | Liu, Yang Li, Qince Wang, Kuanquan Liu, Jun He, Runnan Yuan, Yongfeng Zhang, Henggui |
author_sort | Liu, Yang |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8615597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86155972021-11-26 Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding Liu, Yang Li, Qince Wang, Kuanquan Liu, Jun He, Runnan Yuan, Yongfeng Zhang, Henggui Biosensors (Basel) Article 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. MDPI 2021-11-14 /pmc/articles/PMC8615597/ /pubmed/34821669 http://dx.doi.org/10.3390/bios11110453 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Yang Li, Qince Wang, Kuanquan Liu, Jun He, Runnan Yuan, Yongfeng Zhang, Henggui Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615597/ https://www.ncbi.nlm.nih.gov/pubmed/34821669 http://dx.doi.org/10.3390/bios11110453 |
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