Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network
Impaired blood flow caused by coronary artery occlusion due to thrombus can cause damage to the heart muscle which is often called Myocardial Infarction (MI). To avoid the complexity of MI diseases such as heart failure or arrhythmias that can cause death, it is necessary to diagnose and detect them...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5603 |
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| author | Ahmad Haidar Mirza Siti Nurmaini Radiyati Umi Partan |
| author_facet | Ahmad Haidar Mirza Siti Nurmaini Radiyati Umi Partan |
| author_sort | Ahmad Haidar Mirza |
| collection | DOAJ |
| description | Impaired blood flow caused by coronary artery occlusion due to thrombus can cause damage to the heart muscle which is often called Myocardial Infarction (MI). To avoid the complexity of MI diseases such as heart failure or arrhythmias that can cause death, it is necessary to diagnose and detect them early. An electrocardiogram (ECG) signal is a diagnostic medium that can be used to detect acute MI. Diagnostics with the help of data science is very useful in detecting MI in ECG signals. The purpose of study is to propose an automatic classification framework for Myocardial Infarction (MI) with 15 lead ECG signals consisting of 12 standard leads and 3 Frank leads. This research contributes to the improvement of classification performance for 10 MI classes and normal classes. The PTB dataset trained with the proposed 1D-CNN architecture was able to produce average accuracy, sensitivity, specificity, precision and F1-score of 99.98%, 99.91%, 99.99%, 99.91, and 99.91%. From the evaluation results, it can be concluded that the proposed 1D-CNN architecture is able to provide excellent performance in detecting MI attacks. |
| first_indexed | 2024-03-10T01:29:51Z |
| format | Article |
| id | doaj.art-8ff8c322c07a478183f2aac7ac707b9a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:29:51Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8ff8c322c07a478183f2aac7ac707b9a2023-11-23T13:44:36ZengMDPI AGApplied Sciences2076-34172022-05-011211560310.3390/app12115603Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural NetworkAhmad Haidar Mirza0Siti Nurmaini1Radiyati Umi Partan2Doctoral Program of Engineering Science, Faculty of Engineering, Universitas Sriwijaya, Palembang 30128, IndonesiaIntelligent System Research Group, Universitas Sriwijaya, Palembang 30128, IndonesiaFaculty of Madicine, Universitas Sriwijaya, Palembang 30128, IndonesiaImpaired blood flow caused by coronary artery occlusion due to thrombus can cause damage to the heart muscle which is often called Myocardial Infarction (MI). To avoid the complexity of MI diseases such as heart failure or arrhythmias that can cause death, it is necessary to diagnose and detect them early. An electrocardiogram (ECG) signal is a diagnostic medium that can be used to detect acute MI. Diagnostics with the help of data science is very useful in detecting MI in ECG signals. The purpose of study is to propose an automatic classification framework for Myocardial Infarction (MI) with 15 lead ECG signals consisting of 12 standard leads and 3 Frank leads. This research contributes to the improvement of classification performance for 10 MI classes and normal classes. The PTB dataset trained with the proposed 1D-CNN architecture was able to produce average accuracy, sensitivity, specificity, precision and F1-score of 99.98%, 99.91%, 99.99%, 99.91, and 99.91%. From the evaluation results, it can be concluded that the proposed 1D-CNN architecture is able to provide excellent performance in detecting MI attacks.https://www.mdpi.com/2076-3417/12/11/5603myocardial infarctionCNNECG15 leads |
| spellingShingle | Ahmad Haidar Mirza Siti Nurmaini Radiyati Umi Partan Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network Applied Sciences myocardial infarction CNN ECG 15 leads |
| title | Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network |
| title_full | Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network |
| title_fullStr | Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network |
| title_full_unstemmed | Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network |
| title_short | Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network |
| title_sort | automatic classification of 15 leads ecg signal of myocardial infarction using one dimension convolutional neural network |
| topic | myocardial infarction CNN ECG 15 leads |
| url | https://www.mdpi.com/2076-3417/12/11/5603 |
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