Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387191508> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W4387191508 abstract "Myocardial Infarction (MI) is a cardiovascular disease characterized by the death of the heart muscle. Blockage of blood vessels is one of the causes of myocardial infarction. MI causes blood flow to the heart to become blocked. In the worst case, it causes cardiac death. In general, the screening of cardiovascular disease can be performed using an electrocardiogram (ECG), phonocardiogram (PCG), and photoplethysmogram (PPG). Among these three signals, PCG detection is rarely used in MI detection. In fact, PCG signal has advantages such as non-invasive, efficient, and low-cost. Deep learning is also one of the methods used by many previous researchers to classify an object. However, from several previous research, the implementation of deep learning algorithms on PCG signals, especially in cases of myocardial infarction, has yet to be carried out. As a solution to the problem, this research proposes developing a deep-learning model to predict myocardial infarction based on PCG signals. This research uses the Recurrent Neural Network (RNN) model to classify PCG signal data. PCG signal data was obtained from Hasan Sadikin Hospital Bandung, Indonesia. The experiments showed that the RNN model had a good performance, which resulted in a sensitivity of 95.4%, specificity of 95.2%, and accuracy of 95.3%." @default.
- W4387191508 created "2023-09-30" @default.
- W4387191508 creator A5070266538 @default.
- W4387191508 creator A5070486806 @default.
- W4387191508 creator A5037755335 @default.
- W4387191508 date "2023-08-23" @default.
- W4387191508 modified "2023-09-30" @default.
- W4387191508 title "Myocardial Infarction Prediction Using RNN Deep Learning Algorithm on Phonocardiogram Signals" @default.
- W4387191508 cites W2026735397 @default.
- W4387191508 cites W2076608692 @default.
- W4387191508 cites W2729603194 @default.
- W4387191508 cites W2736570920 @default.
- W4387191508 cites W2787249583 @default.
- W4387191508 cites W2804483946 @default.
- W4387191508 cites W2915954229 @default.
- W4387191508 cites W2977063526 @default.
- W4387191508 cites W3005771459 @default.
- W4387191508 cites W3025833090 @default.
- W4387191508 cites W3081953417 @default.
- W4387191508 cites W3085364681 @default.
- W4387191508 cites W3087507349 @default.
- W4387191508 cites W3093377669 @default.
- W4387191508 cites W3120627055 @default.
- W4387191508 cites W3127996201 @default.
- W4387191508 cites W3135849576 @default.
- W4387191508 cites W3199467252 @default.
- W4387191508 cites W4206572207 @default.
- W4387191508 cites W4210487622 @default.
- W4387191508 cites W4210515614 @default.
- W4387191508 cites W4293232174 @default.
- W4387191508 doi "https://doi.org/10.1109/icoict58202.2023.10262524" @default.
- W4387191508 hasPublicationYear "2023" @default.
- W4387191508 type Work @default.
- W4387191508 citedByCount "0" @default.
- W4387191508 crossrefType "proceedings-article" @default.
- W4387191508 hasAuthorship W4387191508A5037755335 @default.
- W4387191508 hasAuthorship W4387191508A5070266538 @default.
- W4387191508 hasAuthorship W4387191508A5070486806 @default.
- W4387191508 hasConcept C108583219 @default.
- W4387191508 hasConcept C11413529 @default.
- W4387191508 hasConcept C119857082 @default.
- W4387191508 hasConcept C126322002 @default.
- W4387191508 hasConcept C147168706 @default.
- W4387191508 hasConcept C153180895 @default.
- W4387191508 hasConcept C154945302 @default.
- W4387191508 hasConcept C159693508 @default.
- W4387191508 hasConcept C164705383 @default.
- W4387191508 hasConcept C199360897 @default.
- W4387191508 hasConcept C2779843651 @default.
- W4387191508 hasConcept C2780074459 @default.
- W4387191508 hasConcept C28490314 @default.
- W4387191508 hasConcept C41008148 @default.
- W4387191508 hasConcept C500558357 @default.
- W4387191508 hasConcept C50644808 @default.
- W4387191508 hasConcept C71924100 @default.
- W4387191508 hasConceptScore W4387191508C108583219 @default.
- W4387191508 hasConceptScore W4387191508C11413529 @default.
- W4387191508 hasConceptScore W4387191508C119857082 @default.
- W4387191508 hasConceptScore W4387191508C126322002 @default.
- W4387191508 hasConceptScore W4387191508C147168706 @default.
- W4387191508 hasConceptScore W4387191508C153180895 @default.
- W4387191508 hasConceptScore W4387191508C154945302 @default.
- W4387191508 hasConceptScore W4387191508C159693508 @default.
- W4387191508 hasConceptScore W4387191508C164705383 @default.
- W4387191508 hasConceptScore W4387191508C199360897 @default.
- W4387191508 hasConceptScore W4387191508C2779843651 @default.
- W4387191508 hasConceptScore W4387191508C2780074459 @default.
- W4387191508 hasConceptScore W4387191508C28490314 @default.
- W4387191508 hasConceptScore W4387191508C41008148 @default.
- W4387191508 hasConceptScore W4387191508C500558357 @default.
- W4387191508 hasConceptScore W4387191508C50644808 @default.
- W4387191508 hasConceptScore W4387191508C71924100 @default.
- W4387191508 hasLocation W43871915081 @default.
- W4387191508 hasOpenAccess W4387191508 @default.
- W4387191508 hasPrimaryLocation W43871915081 @default.
- W4387191508 hasRelatedWork W2795261237 @default.
- W4387191508 hasRelatedWork W3014300295 @default.
- W4387191508 hasRelatedWork W3164822677 @default.
- W4387191508 hasRelatedWork W4223943233 @default.
- W4387191508 hasRelatedWork W4225161397 @default.
- W4387191508 hasRelatedWork W4312200629 @default.
- W4387191508 hasRelatedWork W4360585206 @default.
- W4387191508 hasRelatedWork W4364306694 @default.
- W4387191508 hasRelatedWork W4380075502 @default.
- W4387191508 hasRelatedWork W4380086463 @default.
- W4387191508 isParatext "false" @default.
- W4387191508 isRetracted "false" @default.
- W4387191508 workType "article" @default.