Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.
<h4>Background</h4>Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must...
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| Format: | Article |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0313390 |
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| author | Vaibhav Gadag Simrat Singh Anshul Harish Khatri Shruti Mishra Sandeep Kumar Satapathy Sung-Bae Cho Abishi Chowdhury Amrit Pal Sachi Nandan Mohanty |
| author_facet | Vaibhav Gadag Simrat Singh Anshul Harish Khatri Shruti Mishra Sandeep Kumar Satapathy Sung-Bae Cho Abishi Chowdhury Amrit Pal Sachi Nandan Mohanty |
| author_sort | Vaibhav Gadag |
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| description | <h4>Background</h4>Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias. To be effective in actual practice, an automated, and computerized detection system for Myocardial Infarction using ECG images, must meet a number of criteria.<h4>Objective</h4>In an actual clinical situation, these requirements-such as dependability, simplicity, and superior decision-making abilities-remain crucial. In the current work, we have developed a model using a dataset that consists of a combination of 928 ECG images taken from publicly available Mendeley Data. It was converted into three classes Myocardial Infarction, Abnormal heartbeat, and Normal.<h4>Methods</h4>The dataset is then imported, pre-processed, and split into a 70:20:10 ratio of training, validation, and testing. It is then trained using the Siamese Network Model.<h4>Results</h4>The classification accuracy comes out to be 98%. The algorithm works excellently with datasets having class imbalance by taking pair of images as input. The validation and testing classification matrix is then generated and the evaluation metrics for both of them come out to be a near-perfect value.<h4>Conclusion</h4>In this study, we developed the ECG signals based early detection of cardiovascular diseases with Siamese network model. |
| format | Article |
| id | doaj-art-dbc375c730f44a078a7ab87690890faf |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-dbc375c730f44a078a7ab87690890faf2025-02-07T05:30:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031339010.1371/journal.pone.0313390Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.Vaibhav GadagSimrat SinghAnshul Harish KhatriShruti MishraSandeep Kumar SatapathySung-Bae ChoAbishi ChowdhuryAmrit PalSachi Nandan Mohanty<h4>Background</h4>Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias. To be effective in actual practice, an automated, and computerized detection system for Myocardial Infarction using ECG images, must meet a number of criteria.<h4>Objective</h4>In an actual clinical situation, these requirements-such as dependability, simplicity, and superior decision-making abilities-remain crucial. In the current work, we have developed a model using a dataset that consists of a combination of 928 ECG images taken from publicly available Mendeley Data. It was converted into three classes Myocardial Infarction, Abnormal heartbeat, and Normal.<h4>Methods</h4>The dataset is then imported, pre-processed, and split into a 70:20:10 ratio of training, validation, and testing. It is then trained using the Siamese Network Model.<h4>Results</h4>The classification accuracy comes out to be 98%. The algorithm works excellently with datasets having class imbalance by taking pair of images as input. The validation and testing classification matrix is then generated and the evaluation metrics for both of them come out to be a near-perfect value.<h4>Conclusion</h4>In this study, we developed the ECG signals based early detection of cardiovascular diseases with Siamese network model.https://doi.org/10.1371/journal.pone.0313390 |
| spellingShingle | Vaibhav Gadag Simrat Singh Anshul Harish Khatri Shruti Mishra Sandeep Kumar Satapathy Sung-Bae Cho Abishi Chowdhury Amrit Pal Sachi Nandan Mohanty Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. PLoS ONE |
| title | Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. |
| title_full | Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. |
| title_fullStr | Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. |
| title_full_unstemmed | Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. |
| title_short | Improving myocardial infarction diagnosis with Siamese network-based ECG analysis. |
| title_sort | improving myocardial infarction diagnosis with siamese network based ecg analysis |
| url | https://doi.org/10.1371/journal.pone.0313390 |
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