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

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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.

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Authors and Affiliations

  1. Department of Electrical, Electronic and Informatics Engineering (DIEEI), University of Catania, Catania, Italy

    Francesco Beritelli, Giacomo Capizzi, Grazia Lo Sciuto & Francesco Scaglione

  2. Department of Mathematics and Informatics, University of Catania, Catania, Italy

    Christian Napoli

Authors
  1. Francesco Beritelli
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  2. Giacomo Capizzi
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  3. Grazia Lo Sciuto
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  4. Christian Napoli
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  5. Francesco Scaglione
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Correspondence to Giacomo Capizzi.

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The authors declare that there is no conflict of interest.

Ethical standard

This paper does not contain any studies with human participants or animals performed by any of the authors. On this matter we would also like to point out that our experiments, concerning data analysis and classification, has not directly been performed on human beings, but were concerning the off-line analysis of the signals obtained by a public database (see https://physionet.org/challenge/2016/) in order to grant more transparency and to use a safe data source also in terms of ethical approval.

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Beritelli, F., Capizzi, G., Lo Sciuto, G. et al. Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks. Biomed. Eng. Lett. 8, 77–85 (2018). https://doi.org/10.1007/s13534-017-0046-z

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  • DOI: https://doi.org/10.1007/s13534-017-0046-z

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