Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification.
Stud Health Technol Inform
; 302: 526-530, 2023 May 18.
Article
em En
| MEDLINE
| ID: mdl-37203741
ABSTRACT
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ruídos Cardíacos
/
Cardiopatias Congênitas
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Stud Health Technol Inform
Assunto da revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Suécia