Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics
Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment l...
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MDPI AG
2023-05-01
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author | Jiguang Shi Zhoutong Li Wenhan Liu Huaicheng Zhang Qianxi Guo Sheng Chang Hao Wang Jin He Qijun Huang |
author_facet | Jiguang Shi Zhoutong Li Wenhan Liu Huaicheng Zhang Qianxi Guo Sheng Chang Hao Wang Jin He Qijun Huang |
author_sort | Jiguang Shi |
collection | DOAJ |
description | Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People’s Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20–99.76%) and 97.62% (95% confidence interval: 96.80–98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices. |
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series | Bioengineering |
spelling | doaj.art-7e1cb1a097e34185a0ab1266ea18db062023-11-18T00:31:58ZengMDPI AGBioengineering2306-53542023-05-0110560710.3390/bioengineering10050607Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease DiagnosticsJiguang Shi0Zhoutong Li1Wenhan Liu2Huaicheng Zhang3Qianxi Guo4Sheng Chang5Hao Wang6Jin He7Qijun Huang8School of Physics and Technology, Wuhan University, Wuhan 430072, ChinaHuangpu Branch of Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaSchool of Physics and Technology, Wuhan University, Wuhan 430072, ChinaMost of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People’s Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20–99.76%) and 97.62% (95% confidence interval: 96.80–98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.https://www.mdpi.com/2306-5354/10/5/607electrocardiogram (ECG)Genetic Algorithm-Based ECG Leads and Segment Length Optimization (GA-LSLO) frameworkportable ECG detection devicescardiovascular disease detection |
spellingShingle | Jiguang Shi Zhoutong Li Wenhan Liu Huaicheng Zhang Qianxi Guo Sheng Chang Hao Wang Jin He Qijun Huang Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics Bioengineering electrocardiogram (ECG) Genetic Algorithm-Based ECG Leads and Segment Length Optimization (GA-LSLO) framework portable ECG detection devices cardiovascular disease detection |
title | Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics |
title_full | Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics |
title_fullStr | Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics |
title_full_unstemmed | Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics |
title_short | Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics |
title_sort | optimized solutions of electrocardiogram lead and segment selection for cardiovascular disease diagnostics |
topic | electrocardiogram (ECG) Genetic Algorithm-Based ECG Leads and Segment Length Optimization (GA-LSLO) framework portable ECG detection devices cardiovascular disease detection |
url | https://www.mdpi.com/2306-5354/10/5/607 |
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