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Published in:
Quality Assessment and Classification of Heart Sounds Using PCG Signals Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Authors : Shahid Ismail Malik, Imran Siddiqi

Published in: Applications of Intelligent Technologies in Healthcare

Publisher: Springer International Publishing

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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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previous chapter Quality Assessment and Classification of Heart Sounds Using PCG Signals
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Metadata
Title
Classification of Normal Heart Beats Using Spectral and Nonspectral Features for Phonocardiography Signals
Authors
Shahid Ismail Malik
Imran Siddiqi
Copyright Year
2019
Book
Applications of Intelligent Technologies in Healthcare

Print ISBN: 978-3-319-96138-5

Electronic ISBN: 978-3-319-96139-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-96139-2_2