1D CNN model for ECG diagnosis based on several classifiers
One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (EC...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Ukrainian |
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Igor Sikorsky Kyiv Polytechnic Institute
2022-12-01
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| Series: | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï |
| Subjects: | |
| Online Access: | http://journal.iasa.kpi.ua/article/view/265099 |
| _version_ | 1797351375419473920 |
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| author | Mahmoud Bassiouni Islam Hegazy Nouhad Rizk El-Sayed El-Dahshan Abdelbadeeh Salem |
| author_facet | Mahmoud Bassiouni Islam Hegazy Nouhad Rizk El-Sayed El-Dahshan Abdelbadeeh Salem |
| author_sort | Mahmoud Bassiouni |
| collection | DOAJ |
| description | One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. |
| first_indexed | 2024-03-08T12:59:36Z |
| format | Article |
| id | doaj.art-ab8291905b7d4b0caf13573b587b0b1a |
| institution | Directory Open Access Journal |
| issn | 1681-6048 2308-8893 |
| language | Ukrainian |
| last_indexed | 2024-03-08T12:59:36Z |
| publishDate | 2022-12-01 |
| publisher | Igor Sikorsky Kyiv Polytechnic Institute |
| record_format | Article |
| series | Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï |
| spelling | doaj.art-ab8291905b7d4b0caf13573b587b0b1a2024-01-19T12:36:00ZukrIgor Sikorsky Kyiv Polytechnic InstituteSistemnì Doslìdženâ ta Informacìjnì Tehnologìï1681-60482308-88932022-12-01472010.20535/SRIT.2308-8893.2022.4.013030461D CNN model for ECG diagnosis based on several classifiersMahmoud Bassiouni0https://orcid.org/0000-0002-8617-8867Islam Hegazy1https://orcid.org/0000-0002-1572-463XNouhad Rizk2https://orcid.org/0000-0001-9277-9741El-Sayed El-Dahshan3https://orcid.org/0000-0002-1221-0262Abdelbadeeh Salem4https://orcid.org/0000-0003-0268-6539Egyptian E-Learning University (EELU), El-GizaAin Shams University, CairoHouston UniversityAin Shams University, CairoAin Shams University, CairoOne of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.http://journal.iasa.kpi.ua/article/view/265099electrocardiogram (ecg)continuous wavelet transform (cwt)1d convolutional neural network (cnn) model |
| spellingShingle | Mahmoud Bassiouni Islam Hegazy Nouhad Rizk El-Sayed El-Dahshan Abdelbadeeh Salem 1D CNN model for ECG diagnosis based on several classifiers Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï electrocardiogram (ecg) continuous wavelet transform (cwt) 1d convolutional neural network (cnn) model |
| title | 1D CNN model for ECG diagnosis based on several classifiers |
| title_full | 1D CNN model for ECG diagnosis based on several classifiers |
| title_fullStr | 1D CNN model for ECG diagnosis based on several classifiers |
| title_full_unstemmed | 1D CNN model for ECG diagnosis based on several classifiers |
| title_short | 1D CNN model for ECG diagnosis based on several classifiers |
| title_sort | 1d cnn model for ecg diagnosis based on several classifiers |
| topic | electrocardiogram (ecg) continuous wavelet transform (cwt) 1d convolutional neural network (cnn) model |
| url | http://journal.iasa.kpi.ua/article/view/265099 |
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