Automatic classification of ECG heartbeats using deep neural networks

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DC Field	Value	Language
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
Appears in Collections:	Master

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