Document Detail

Autonomous detection of heart sound abnormalities using an auscultation jacket.
MedLine Citation:
PMID:  20169844     Owner:  NLM     Status:  MEDLINE    
This paper presents a study using an auscultation jacket with embedded electronic stethoscopes, and a software classification system capable of differentiating between normal and certain auscultatory abnormalities. The aim of the study is to demonstrate the potential of such a system for semi-automated diagnosis for underserved locations, for instance in rural areas or in developing countries where patients far outnumber the available medical personnel. Using an "auscultation jacket", synchronous data was recorded at multiple chest locations on 31 healthy volunteers and 21 patients with heart pathologies. Electrocardiograms (ECGs) were also recorded simultaneously with phonocardiographic data. Features related to heart pathologies were extracted from the signals and used as input to a feed-forward artificial neural network. The system is able to classify between normal and certain abnormal heart sounds with a sensitivity of 84% and a specificity of 86%. Though the number of training and testing samples presented are limited, the system performed well in differentiating between normal and abnormal heart sounds in the given database of available recordings. The results of this study demonstrate the potential of such a system to be used as a fast and cost-effective screening tool for heart pathologies.
C Visagie; C Scheffer; W W Lubbe; A F Doubell
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Australasian physical & engineering sciences in medicine / supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine     Volume:  32     ISSN:  0158-9938     ISO Abbreviation:  Australas Phys Eng Sci Med     Publication Date:  2009 Dec 
Date Detail:
Created Date:  2010-02-22     Completed Date:  2010-04-02     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8208130     Medline TA:  Australas Phys Eng Sci Med     Country:  Australia    
Other Details:
Languages:  eng     Pagination:  240-50     Citation Subset:  IM    
Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Matieland 7602, South Africa.
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MeSH Terms
Artificial Intelligence*
Diagnosis, Computer-Assisted / instrumentation,  methods*
Equipment Design
Equipment Failure Analysis
Heart Auscultation / instrumentation*,  methods*
Heart Valve Diseases / diagnosis*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Sound Spectrography / instrumentation,  methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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