Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method

Daoud Boutana; M. Benidir; B. Barkat

Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method

Daoud Boutana
, M. Benidir
, B. Barkat

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

The Phonocardiogram (PCG) is the graphical representation of acoustic energy due to the mechanical cardiac activity. Sometimes cardiac diseases provide pathological murmurs mixed with the main components of the Heart Sound Signal (HSs). The Empirical Mode Decomposition (EMD) allows decomposing a multicomponent signal into a set of monocomponent signals, called Intrinsic Mode Functions (IMFs). Each IMF represents an oscillatory mode with one instantaneous frequency. The goal of this paper is to segment some pathological HSs by selecting the most appropriate IMFs using the correlation coefficient. Then we extract some time-frequency characteristics considered as useful parameters to distinguish different cases of heart diseases. The experimental results conducted on some real-life pathological HSs such as: Mitral Regurgitation (MR), Aortic Regurgitation (AR) and the Opening Snap (OS) case; revealed the performance of the proposed method.

Original language	British English
Title of host publication	2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
Pages	1437-1441
Number of pages	5
ISBN (Electronic)	9780992862619
State	Published - 10 Nov 2014
Event	22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal Duration: 1 Sep 2014 → 5 Sep 2014

Publication series

Name	European Signal Processing Conference
ISSN (Print)	2219-5491

Conference

Conference	22nd European Signal Processing Conference, EUSIPCO 2014
Country/Territory	Portugal
City	Lisbon
Period	1/09/14 → 5/09/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

correlation function
Empirical mode decomposition
heart sound signal
pathological murmurs

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Cite this

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title = "Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method",

abstract = "The Phonocardiogram (PCG) is the graphical representation of acoustic energy due to the mechanical cardiac activity. Sometimes cardiac diseases provide pathological murmurs mixed with the main components of the Heart Sound Signal (HSs). The Empirical Mode Decomposition (EMD) allows decomposing a multicomponent signal into a set of monocomponent signals, called Intrinsic Mode Functions (IMFs). Each IMF represents an oscillatory mode with one instantaneous frequency. The goal of this paper is to segment some pathological HSs by selecting the most appropriate IMFs using the correlation coefficient. Then we extract some time-frequency characteristics considered as useful parameters to distinguish different cases of heart diseases. The experimental results conducted on some real-life pathological HSs such as: Mitral Regurgitation (MR), Aortic Regurgitation (AR) and the Opening Snap (OS) case; revealed the performance of the proposed method.",

keywords = "correlation function, Empirical mode decomposition, heart sound signal, pathological murmurs",

author = "Daoud Boutana and M. Benidir and B. Barkat",

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year = "2014",

month = nov,

day = "10",

language = "British English",

series = "European Signal Processing Conference",

pages = "1437--1441",

booktitle = "2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014",

}

Boutana, D, Benidir, M & Barkat, B 2014, Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method. in 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014., 6952507, European Signal Processing Conference, pp. 1437-1441, 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, Portugal, 1/09/14.

Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method. / Boutana, Daoud; Benidir, M.; Barkat, B.
2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014. 2014. p. 1437-1441 6952507 (European Signal Processing Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method

AU - Boutana, Daoud

AU - Benidir, M.

AU - Barkat, B.

PY - 2014/11/10

Y1 - 2014/11/10

N2 - The Phonocardiogram (PCG) is the graphical representation of acoustic energy due to the mechanical cardiac activity. Sometimes cardiac diseases provide pathological murmurs mixed with the main components of the Heart Sound Signal (HSs). The Empirical Mode Decomposition (EMD) allows decomposing a multicomponent signal into a set of monocomponent signals, called Intrinsic Mode Functions (IMFs). Each IMF represents an oscillatory mode with one instantaneous frequency. The goal of this paper is to segment some pathological HSs by selecting the most appropriate IMFs using the correlation coefficient. Then we extract some time-frequency characteristics considered as useful parameters to distinguish different cases of heart diseases. The experimental results conducted on some real-life pathological HSs such as: Mitral Regurgitation (MR), Aortic Regurgitation (AR) and the Opening Snap (OS) case; revealed the performance of the proposed method.

AB - The Phonocardiogram (PCG) is the graphical representation of acoustic energy due to the mechanical cardiac activity. Sometimes cardiac diseases provide pathological murmurs mixed with the main components of the Heart Sound Signal (HSs). The Empirical Mode Decomposition (EMD) allows decomposing a multicomponent signal into a set of monocomponent signals, called Intrinsic Mode Functions (IMFs). Each IMF represents an oscillatory mode with one instantaneous frequency. The goal of this paper is to segment some pathological HSs by selecting the most appropriate IMFs using the correlation coefficient. Then we extract some time-frequency characteristics considered as useful parameters to distinguish different cases of heart diseases. The experimental results conducted on some real-life pathological HSs such as: Mitral Regurgitation (MR), Aortic Regurgitation (AR) and the Opening Snap (OS) case; revealed the performance of the proposed method.

KW - correlation function

KW - Empirical mode decomposition

KW - heart sound signal

KW - pathological murmurs

UR - https://www.scopus.com/pages/publications/84911868736

M3 - Conference contribution

AN - SCOPUS:84911868736

T3 - European Signal Processing Conference

SP - 1437

EP - 1441

BT - 2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014

T2 - 22nd European Signal Processing Conference, EUSIPCO 2014

Y2 - 1 September 2014 through 5 September 2014

ER -

Segmentation and time-frequency analysis of pathological Heart Sound Signals using the EMD method

Abstract

Publication series

Conference

UN SDGs

Keywords

OpenUrl availability

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