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Robust PVC Identification by Fusing Expert System and Deep Learning

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually e...

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Autores principales: Cai, Zhipeng, Wang, Tiantian, Shen, Yumin, Xing, Yantao, Yan, Ruqiang, Li, Jianqing, Liu, Chengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025768/
https://www.ncbi.nlm.nih.gov/pubmed/35448245
http://dx.doi.org/10.3390/bios12040185
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author Cai, Zhipeng
Wang, Tiantian
Shen, Yumin
Xing, Yantao
Yan, Ruqiang
Li, Jianqing
Liu, Chengyu
author_facet Cai, Zhipeng
Wang, Tiantian
Shen, Yumin
Xing, Yantao
Yan, Ruqiang
Li, Jianqing
Liu, Chengyu
author_sort Cai, Zhipeng
collection PubMed
description Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.
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spelling pubmed-90257682022-04-23 Robust PVC Identification by Fusing Expert System and Deep Learning Cai, Zhipeng Wang, Tiantian Shen, Yumin Xing, Yantao Yan, Ruqiang Li, Jianqing Liu, Chengyu Biosensors (Basel) Article Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings. MDPI 2022-03-22 /pmc/articles/PMC9025768/ /pubmed/35448245 http://dx.doi.org/10.3390/bios12040185 Text en © 2022 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
Cai, Zhipeng
Wang, Tiantian
Shen, Yumin
Xing, Yantao
Yan, Ruqiang
Li, Jianqing
Liu, Chengyu
Robust PVC Identification by Fusing Expert System and Deep Learning
title Robust PVC Identification by Fusing Expert System and Deep Learning
title_full Robust PVC Identification by Fusing Expert System and Deep Learning
title_fullStr Robust PVC Identification by Fusing Expert System and Deep Learning
title_full_unstemmed Robust PVC Identification by Fusing Expert System and Deep Learning
title_short Robust PVC Identification by Fusing Expert System and Deep Learning
title_sort robust pvc identification by fusing expert system and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025768/
https://www.ncbi.nlm.nih.gov/pubmed/35448245
http://dx.doi.org/10.3390/bios12040185
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