Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network
| Autor: | Johannes de Bie, Ricardo Salinas-Martinez, Frida Sandberg, Nicoletta Marzocchi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science Representation (systemics) Atrial fibrillation Pattern recognition Image segmentation 030204 cardiovascular system & hematology medicine.disease Clinical routine Convolutional neural network Cryptogenic stroke 03 medical and health sciences 0302 clinical medicine Electronic countermeasure medicine Cardiac and Cardiovascular Systems cardiovascular diseases Artificial intelligence business Electrocardiography 030217 neurology & neurosurgery |
| Zdroj: | CinC 2020 Computing in Cardiology Conference (CinC) |
| ISSN: | 2325-887X |
| DOI: | 10.22489/cinc.2020.170 |
| Popis: | Long-term electrocardiogram (ECG) monitoring is a standard clinical routine in cryptogenic stroke survivors to assess the presence of atrial fibrillation (AF). However, manual evaluation of such recordings is time consuming, in particular when brief episodes are of interest. The electrocardiomatrix (ECM) technique allows compact, two-dimensional representation of the ECG and facilitates its review. In this study, we present a convolutional neural network (CNN) approach for automatic detection of AF based on ECM images. ECG segments of only 10 beats were converted into ECM images. A CNN was implemented to classify the ECMs between non-AF and AF. The CNN was trained using the MIT-BIH-NSR and the MIT-BIH-LTAF, and tested on the MIT-BIH-AF. A total of 120088 non-AF and 108088 AF ECM images were classified with accuracy of 86.95%. This study suggests that a CNN allows automatic detection of AF episodes of only 10 beats when the ECG data is represented as an ECM image. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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