Models, systems, networks in economics, engineering, nature and society
Background. The article is devoted to the development of a neural network for ECG signals classification. Automatic classification of ECG signals frees cardiologists from laborious and monotonous work and reduces the time of ECG interpretation. The aim of the study is to create and evaluate a convol...
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| Format: | Article |
| Language: | English |
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Penza State University Publishing House
2025-02-01
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| Series: | Модели, системы, сети в экономике, технике, природе и обществе |
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| author | L.Yu. Кrivonogov1 S.F. Levin I.S. Inomboev D.V. Papshev |
| author_facet | L.Yu. Кrivonogov1 S.F. Levin I.S. Inomboev D.V. Papshev |
| author_sort | L.Yu. Кrivonogov1 |
| collection | DOAJ |
| description | Background. The article is devoted to the development of a neural network for ECG signals classification. Automatic classification of ECG signals frees cardiologists from laborious and monotonous work and reduces the time of ECG interpretation. The aim of the study is to create and evaluate a convolutional neural network model for automatic ECG signals classification in 12 standard leads to identify the most common and dangerous cardiovascular diseases. Materials and methods. Groups of diseases for classification were selected and substantiated. An original, modified architecture of the 1D ResNet34 convolutional neural network was proposed. ECG recordings from the publicly available Chinese CPSC Database were used to train and test the model. The training and evaluation of the model's performance were carried out using the 10-fold cross-validation method. Results. The performance of ECG signal classification was evaluated using standard metrics. The average values of accuracy, F1 score, and AUC-ROC for the developed classifier are 0.964, 0.832, and 0.975, respectively. Conclusions. The performance of the model corresponds to the world level of the best global achievements and is comparable to the expert-medical level. The developed ECG signal classifier can be integrated into various electrocardiographic diagnostic systems. |
| format | Article |
| id | doaj-art-690a759c577c4c3292c0eb1755d89fb7 |
| institution | DOAJ |
| issn | 2227-8486 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Penza State University Publishing House |
| record_format | Article |
| series | Модели, системы, сети в экономике, технике, природе и обществе |
| spelling | doaj-art-690a759c577c4c3292c0eb1755d89fb72025-08-20T02:56:58ZengPenza State University Publishing HouseМодели, системы, сети в экономике, технике, природе и обществе2227-84862025-02-01410812110.21685/2227-8486-2024-4-9Models, systems, networks in economics, engineering, nature and societyL.Yu. Кrivonogov10S.F. Levin1I.S. Inomboev2D.V. Papshev3Penza State UniversityPenza State UniversityPenza State UniversityPenza State UniversityBackground. The article is devoted to the development of a neural network for ECG signals classification. Automatic classification of ECG signals frees cardiologists from laborious and monotonous work and reduces the time of ECG interpretation. The aim of the study is to create and evaluate a convolutional neural network model for automatic ECG signals classification in 12 standard leads to identify the most common and dangerous cardiovascular diseases. Materials and methods. Groups of diseases for classification were selected and substantiated. An original, modified architecture of the 1D ResNet34 convolutional neural network was proposed. ECG recordings from the publicly available Chinese CPSC Database were used to train and test the model. The training and evaluation of the model's performance were carried out using the 10-fold cross-validation method. Results. The performance of ECG signal classification was evaluated using standard metrics. The average values of accuracy, F1 score, and AUC-ROC for the developed classifier are 0.964, 0.832, and 0.975, respectively. Conclusions. The performance of the model corresponds to the world level of the best global achievements and is comparable to the expert-medical level. The developed ECG signal classifier can be integrated into various electrocardiographic diagnostic systems.electrocardiographyecg signal classificationdeep learningconvolutional neural networks |
| spellingShingle | L.Yu. Кrivonogov1 S.F. Levin I.S. Inomboev D.V. Papshev Models, systems, networks in economics, engineering, nature and society Модели, системы, сети в экономике, технике, природе и обществе electrocardiography ecg signal classification deep learning convolutional neural networks |
| title | Models, systems, networks in economics, engineering, nature and society |
| title_full | Models, systems, networks in economics, engineering, nature and society |
| title_fullStr | Models, systems, networks in economics, engineering, nature and society |
| title_full_unstemmed | Models, systems, networks in economics, engineering, nature and society |
| title_short | Models, systems, networks in economics, engineering, nature and society |
| title_sort | models systems networks in economics engineering nature and society |
| topic | electrocardiography ecg signal classification deep learning convolutional neural networks |
| work_keys_str_mv | AT lyukrivonogov1 modelssystemsnetworksineconomicsengineeringnatureandsociety AT sflevin modelssystemsnetworksineconomicsengineeringnatureandsociety AT isinomboev modelssystemsnetworksineconomicsengineeringnatureandsociety AT dvpapshev modelssystemsnetworksineconomicsengineeringnatureandsociety |