Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks
Biomedical Engineering Letters
; (4): 77-85, 2018.
Article
en En
| WPRIM
| ID: wpr-739416
Biblioteca responsable:
WPRO
ABSTRACT
The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.
Palabras clave
Texto completo:
1
Base de datos:
WPRIM
Asunto principal:
Sensibilidad y Especificidad
/
Ruidos Cardíacos
/
Clasificación
/
Diagnóstico
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Conjunto de Datos
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Análisis de Fourier
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Corazón
/
Cardiopatías
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Biomedical Engineering Letters
Año:
2018
Tipo del documento:
Article