A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree
Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.1008111/full |
| _version_ | 1811191271567392768 |
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| author | Lijuan Chou Lijuan Chou Jicheng Liu Shengrong Gong Shengrong Gong Yongxin Chou |
| author_facet | Lijuan Chou Lijuan Chou Jicheng Liu Shengrong Gong Shengrong Gong Yongxin Chou |
| author_sort | Lijuan Chou |
| collection | DOAJ |
| description | Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias. |
| first_indexed | 2024-04-11T15:03:35Z |
| format | Article |
| id | doaj.art-349a05f78e8d470bb5422d919eb2409c |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-11T15:03:35Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-349a05f78e8d470bb5422d919eb2409c2022-12-22T04:16:52ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-10-011310.3389/fphys.2022.10081111008111A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision treeLijuan Chou0Lijuan Chou1Jicheng Liu2Shengrong Gong3Shengrong Gong4Yongxin Chou5School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing, ChinaSchool of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing, ChinaSchool of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, ChinaSchool of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, ChinaExtreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.https://www.frontiersin.org/articles/10.3389/fphys.2022.1008111/fullpulse rate variabilityarterial blood pressurecardiovascular diseaseslife-threatening arrhythmiasdecision treeintelligent recognition |
| spellingShingle | Lijuan Chou Lijuan Chou Jicheng Liu Shengrong Gong Shengrong Gong Yongxin Chou A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree Frontiers in Physiology pulse rate variability arterial blood pressure cardiovascular diseases life-threatening arrhythmias decision tree intelligent recognition |
| title | A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree |
| title_full | A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree |
| title_fullStr | A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree |
| title_full_unstemmed | A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree |
| title_short | A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree |
| title_sort | life threatening arrhythmia detection method based on pulse rate variability analysis and decision tree |
| topic | pulse rate variability arterial blood pressure cardiovascular diseases life-threatening arrhythmias decision tree intelligent recognition |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.1008111/full |
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