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Classification of Normal Heart Beats Using Spectral and Nonspectral Features for Phonocardiography Signals

  • Shahid Ismail Malik
  • Imran SiddiqiEmail author
Chapter
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

This study explores the features based on spectrum, energy, and probability for heart beat classification using PCG signals. Features extracted from heart beat signals are fed to a feed-forward artificial neural network to discriminate between the heart sounds S1, S2, and noise. Evaluations are carried out on a publicly available dataset, and the system performance on individual as well as combined features is studied with and without the application of principal component analysis (PCA). An average classification rate of around 84% is reported, and high classification rates are maintained by using only a small proportion of the feature set.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Army Public College of Management and SciencesRawalpindiPakistan
  2. 2.Bahria UniversityIslamabadPakistan

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