Sökning: L773:9781643683881 OR L773:9781643683898 >
Parralel Recurrent ...
Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification
-
- Gharehbaghi, Arash, 1972- (författare)
- School of Information Technology, Halmstad University, Halmstad, Sweden
-
- Partovi, Elaheh (författare)
- Department of Electrical Engineering, Amirkabir University, Tehran, Iran
-
- Babic, Ankica, 1960- (författare)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Department Information Science and Media Studies, University of Bergen, Bergen, Norway
-
(creator_code:org_t)
- IOS PRESS, 2023
- 2023
- Engelska.
-
Ingår i: CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023. - : IOS PRESS. - 9781643683898 ; , s. 526-530
- Relaterad länk:
-
https://liu.diva-por... (primary) (Raw object)
-
visa fler...
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
visa färre...
Abstract
Ämnesord
Stäng
- This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Heart sound; convolutional neural networks; deep learning; intelligent phonocardiography; parallel convolutional neural network
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas