Loughborough University
Browse
Chambers 6.pdf (175.17 kB)

Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound

Download (175.17 kB)
conference contribution
posted on 2009-11-30, 13:48 authored by Thato K. Tsalaile, Mohsen Naqvi, K. Nazarpour, Saeid Sanei, Jonathon Chambers
A novel approach for separating heart sound signals (HSSs) from lung sound recordings is presented. The approach is based on blind source extraction (BSE) with second-order statistics (SOS), which exploits the quasi-periodicity of the HSSs. The method is evaluated on both synthetic periodic signals of known period mixed with temporally white Gaussian noise (WGN) as well as on real quasi periodic HSSs mixed with lung sound signals (LSSs). Qualitative evaluation involving comparison of the power spectral densities (PSDs) of the extracted signals, by the proposed method and by the JADE algorithm, and that of the original signal is performed for the case of real data. Separation results confirm the utility of the proposed approach, although departure from strict periodicity may impact performance.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

TSALAILE, T. ... et al, 2008. Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound. IN: Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing 2008. ICASSP 2008, Las Vegas, Nevada, 31 March-4 April 2008, pp. 461-464.

Publisher

© IEEE

Version

  • VoR (Version of Record)

Publication date

2008

Notes

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

9781424414833

ISSN

1520-6149

Language

  • en