Literature DB >> 18002196

Third heart sound detection using wavelet transform-simplicity filter.

D Kumar1, P Carvalho, M Antunes, J Henriques, A Sá e Melo, R Schmidt, J Habetha.   

Abstract

Heart failure and heart valvar diseases are chronic heart disorders which are potentially diagnosed using heart sound characteristics. Heart sound components S1 and S2 exhibit significant characteristics for valvar dysfunction while pathological S3 sound is a prominent sign for heart failure in elderly people. In this paper, a new automatic detection method of the S3 heart sound is proposed. The method is build upon wavelet transform-simplicity filter which separates S1, S2 and S3 sounds from background noise enabling heart sound segmentation even in the presence of heart murmurs or noise sources. The algorithm uses physiologically inspired criteria to assess the presence of S3 heart sound components and to perform their segmentation. Heart sound samples recorded from children as well as from elderly patients with heart failure were used to test the method. The achieved sensitivity and specificity were 90.35% and 92.35%, respectively.

Entities:  

Mesh:

Year:  2007        PMID: 18002196     DOI: 10.1109/IEMBS.2007.4352530

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  6 in total

1.  An automatic segmentation method for heart sounds.

Authors:  Qingshu Liu; Xiaomei Wu; Xiaojing Ma
Journal:  Biomed Eng Online       Date:  2018-08-06       Impact factor: 2.819

2.  Detection of the third and fourth heart sounds using Hilbert-Huang transform.

Authors:  Yi-Li Tseng; Pin-Yu Ko; Fu-Shan Jaw
Journal:  Biomed Eng Online       Date:  2012-02-14       Impact factor: 2.819

3.  Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study.

Authors:  Amirtaha Taebi; Hansen A Mansy
Journal:  Bioengineering (Basel)       Date:  2017-04-07

4.  Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders.

Authors:  Amzad Hossain; Sharif Uddin; Parinda Rahman; Meratun Junnut Anee; Md Mehedi Hasan Rifat; M Monir Uddin
Journal:  Biomed Res Int       Date:  2022-07-27       Impact factor: 3.246

5.  Efficiency, sensitivity and specificity of automated auscultation diagnosis device for detection and discrimination of cardiac murmurs in children.

Authors:  Armen Kocharian; Amir-Ahmad Sepehri; Azin Janani; Elaheh Malakan-Rad
Journal:  Iran J Pediatr       Date:  2013-08       Impact factor: 0.364

6.  Transfer Learning Models for Detecting Six Categories of Phonocardiogram Recordings.

Authors:  Miao Wang; Binbin Guo; Yating Hu; Zehang Zhao; Chengyu Liu; Hong Tang
Journal:  J Cardiovasc Dev Dis       Date:  2022-03-16
  6 in total

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