Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature ventricular contraction, atrial premature contrac...

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Detalles Bibliográficos
Autores principales: Kim, Jeong-Hwan, Seo, Seung-Yeon, Song, Chul-Gyu, Kim, Kyeong-Seop
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512052/
https://www.ncbi.nlm.nih.gov/pubmed/31183029
http://dx.doi.org/10.1155/2019/2826901

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512052/
https://www.ncbi.nlm.nih.gov/pubmed/31183029
http://dx.doi.org/10.1155/2019/2826901

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