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CNN Networks to Classify Cardiopulmonary Signals
Conference on Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) ; 2022.
Article in Spanish | Web of Science | ID: covidwho-1985445
ABSTRACT
At the moment, the world lives in a pandemic situation of COVID-19 and related variants, driving urgent needs for expanded assessments. A complementary support of related healthcare can be based on an intelligent system that can diagnose early onset of respiratory disorders. The convolutional neural networks (CNN) were implemented utilizing image data, reflecting bidimensional signals. Specifically, CNN has shown to be powerful tool in the context of cardiopulmonary sounds evaluation. The configurations of CNN contain convolutional layers to extract feature maps and fully connected layers to classify indicators of interest. Even though, learning algorithms use parameters like learning rate which can determine and attain CNN configuration less complex, with excellent results as reflected in the experiments we carried out, and which focused on achieved configuration of CNN with excellent results classifying heart sounds (HS) and lung sounds (LS).
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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: Spanish Journal: Pan American Health Care Exchanges (GMEPE Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: Spanish Journal: Pan American Health Care Exchanges (GMEPE Year: 2022 Document Type: Article