Convolutional Neural Network for Improvement of Heart Valve Disease Detection

Authors

  • Blanca Tovar-Corona Instituto Politécnico Nacional
  • Santiago Isaac Flores-Alonso Instituto Politécnico Nacional
  • René Luna-García Instituto Politécnico Nacional

DOI:

https://doi.org/10.13053/cys-26-3-4202

Keywords:

Artificial intelligence, deep neural network, phonocardiography, heart valve disease

Abstract

Valvular heart disease (VHD) encompasses a number of common cardiovascular conditions that account for a significant percentage of heart diseases. At present, the acoustic phenomena generated by the abnormal functioning of the heart valves can be recorded and digitized using electronic stethoscopes known as phonocardiographs. The analysis of thephonocardiographic signals has made it possible to indicate that the normal and pathological records differ from each other in terms of both temporal and spectral characteristics. The present work describes the construction and implementation of a Deep Learning (DL) algorithm for the binary classification of normal and abnormal heart sounds. The performance of this approach reached an accuracy higher than 98 % and specificities in the ”Normal” class of up to 99 %.

Author Biographies

Blanca Tovar-Corona, Instituto Politécnico Nacional

UPIITA Posgraduate Department, Academic/Researcher

Santiago Isaac Flores-Alonso, Instituto Politécnico Nacional

CIC AI laboratory. Ph.D. Student

René Luna-García, Instituto Politécnico Nacional

CIC AI laboratory, Academic/Researcher

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Published

2022-08-28

Issue

Vol. 26 No. 3 (2022): 26(3) 2022

Section

Articles

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