Thèses en ligne de l'université 8 Mai 1945 Guelma
Automatic classification of ECG heartbeats using deep neural networks
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| dc.contributor.author | Sadoun, Mohammed Seghir | |
| dc.date.accessioned | 2022-10-12T08:23:34Z | |
| dc.date.available | 2022-10-12T08:23:34Z | |
| dc.date.issued | 2022-06 | |
| dc.identifier.uri | http://dspace.univ-guelma.dz/jspui/handle/123456789/13119 | |
| dc.description.abstract | Automatic ECG classification systems are a valuable tool for assisting doctors and su- pervising patients. Deep neural networks have been widely used as an alternative to the existing classification systems based on distinct feature extraction and classification phases. In this study, we propose an automatic system that classifies ECG heartbeats based on 1D convolutional neural network. To evaluate the proposed model, we perform tests on the MIT-BIH arrhythmia database and we considers four classes | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Université 08 MAI 1945 Guelma | en_US |
| dc.subject | Electrocardiography (ECG), Deep neural network, 1D Convolutional neural network (CNN) | en_US |
| dc.title | Automatic classification of ECG heartbeats using deep neural networks | en_US |
| dc.type | Working Paper | en_US |
