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Classification of homomorphic segmented phonocardiogram signals using grow and learn network

Gupta, C.N., Palaniappan, Ramaswamy, Swaminathan, S. (2005) Classification of homomorphic segmented phonocardiogram signals using grow and learn network. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. . pp. 4251-4254. IEEE ISBN 978-0-7803-8740-9. (doi:10.1109/IEMBS.2005.1615403) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/IEMBS.2005.1615403

Abstract

A segmentation algorithm, which detects a single cardiac cycle (S 1-Systole-S2-Diastole) of Phonocardiogram (PCG) signals using Homomorphic filtering and K-means clustering and a three way classification of heart sounds into Normal (N), Systolic murmur (S) and Diastolic murmur (D) using Grow and Learn (GAL) neural network, are presented. Homomorphic filtering converts a non-linear combination of signals (multiplied in time domain) into a linear combination by applying logarithmic transformation. It involves the retrieval of the envelope, a(n) of the PCG signal by attenuating the contribution of fast varying component, f(n) using an appropriate low pass filter. K-means clustering is a non-hierarchical partitioning method, which helps to indicate single cardiac cycle in the PCG signal. Segmentation performance of 90.45% was achieved using the proposed algorithm. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. Grow and Learn network was used for classification of the segmented PCG signals and a classification accuracy of 97.02% was achieved. It is concluded that Homomorphic filtering and GAL network could be used for segmentation and classification of PCG signals without using a reference signal.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/IEMBS.2005.1615403
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Annu Int Conf IEEE Eng Med Biol Proc [Field not mapped to EPrints] AD - Biomedical Engineering Research Center, Nanyang Technological University, Singapore 639815, Singapore [Field not mapped to EPrints] AD - Department of Computer Science, University of Essex, Colchester, United Kingdom [Field not mapped to EPrints] AD - School of Electrical and Electronic Engineering, Biomedical Engineering Research Center, Nanyang Technological University, Singapore 639815, Singapore [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] C3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings [Field not mapped to EPrints]
Uncontrolled keywords: Acoustic wave propagation, Algorithms, Image segmentation, Neural networks, Wavelet transforms, Heart sounds, Homomorphic filtering, Logarithmic transformation, Phonocardiogram signals, Bioelectric phenomena
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 12:07 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70744 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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