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.

Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

BERITELLI, FRANCESCO;CAPIZZI, GIACOMO
;
LO SCIUTO, GRAZIA;NAPOLI, CHRISTIAN;Scaglione, Francesco
2018-01-01

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.
Heart sounds, Phonocardiogram, Cardiac signal analysis, Gram polynomials, Probabilistic neural network
File in questo prodotto:
File Dimensione Formato  
Automatic heart activity.pdf

non disponibili

Tipologia: Versione Editoriale (PDF)
Dimensione 995.26 kB
Formato Adobe PDF
995.26 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/310319
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 54
  • ???jsp.display-item.citation.isi??? ND
social impact