Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requi...

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Autores principales:	Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Patricia Angela R. Abu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Electrocardiogram (ECG) convolutional neural network premature ventricular contraction OTSU ECG classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/818893dc77fb4059bc6f38bee7cfa8a7
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spelling	oai:doaj.org-article:818893dc77fb4059bc6f38bee7cfa8a72021-12-02T00:00:16ZAutomated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection2169-353610.1109/ACCESS.2021.3128736https://doaj.org/article/818893dc77fb4059bc6f38bee7cfa8a72021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617645/https://doaj.org/toc/2169-3536Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat.Liang-Hung WangLin-Juan DingChao-Xin XieSu-Ya JiangI-Chun KuoXin-Kang WangJie GaoPao-Cheng HuangPatricia Angela R. AbuIEEEarticleElectrocardiogram (ECG)convolutional neural networkpremature ventricular contractionOTSUECG classificationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156581-156591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrocardiogram (ECG) convolutional neural network premature ventricular contraction OTSU ECG classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrocardiogram (ECG) convolutional neural network premature ventricular contraction OTSU ECG classification Electrical engineering. Electronics. Nuclear engineering TK1-9971 Liang-Hung Wang Lin-Juan Ding Chao-Xin Xie Su-Ya Jiang I-Chun Kuo Xin-Kang Wang Jie Gao Pao-Cheng Huang Patricia Angela R. Abu Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection
description	Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat.
format	article
author	Liang-Hung Wang Lin-Juan Ding Chao-Xin Xie Su-Ya Jiang I-Chun Kuo Xin-Kang Wang Jie Gao Pao-Cheng Huang Patricia Angela R. Abu
author_facet	Liang-Hung Wang Lin-Juan Ding Chao-Xin Xie Su-Ya Jiang I-Chun Kuo Xin-Kang Wang Jie Gao Pao-Cheng Huang Patricia Angela R. Abu
author_sort	Liang-Hung Wang
title	Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection
title_short	Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection
title_full	Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection
title_fullStr	Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection
title_full_unstemmed	Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection
title_sort	automated classification model with otsu and cnn method for premature ventricular contraction detection
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/818893dc77fb4059bc6f38bee7cfa8a7
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Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

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