Arrhythmia classification detection based on multiple electrocardiograms databases.
According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and im...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0290995 |
| _version_ | 1797666839324524544 |
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| author | Meng Qi Hongxiang Shao Nianfeng Shi Guoqiang Wang Yifei Lv |
| author_facet | Meng Qi Hongxiang Shao Nianfeng Shi Guoqiang Wang Yifei Lv |
| author_sort | Meng Qi |
| collection | DOAJ |
| description | According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%. |
| first_indexed | 2024-03-11T20:04:20Z |
| format | Article |
| id | doaj.art-4e5ebdf990a54c94b50ab749b23e3ea5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-11T20:04:20Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4e5ebdf990a54c94b50ab749b23e3ea52023-10-04T05:31:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01189e029099510.1371/journal.pone.0290995Arrhythmia classification detection based on multiple electrocardiograms databases.Meng QiHongxiang ShaoNianfeng ShiGuoqiang WangYifei LvAccording to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.https://doi.org/10.1371/journal.pone.0290995 |
| spellingShingle | Meng Qi Hongxiang Shao Nianfeng Shi Guoqiang Wang Yifei Lv Arrhythmia classification detection based on multiple electrocardiograms databases. PLoS ONE |
| title | Arrhythmia classification detection based on multiple electrocardiograms databases. |
| title_full | Arrhythmia classification detection based on multiple electrocardiograms databases. |
| title_fullStr | Arrhythmia classification detection based on multiple electrocardiograms databases. |
| title_full_unstemmed | Arrhythmia classification detection based on multiple electrocardiograms databases. |
| title_short | Arrhythmia classification detection based on multiple electrocardiograms databases. |
| title_sort | arrhythmia classification detection based on multiple electrocardiograms databases |
| url | https://doi.org/10.1371/journal.pone.0290995 |
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