liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Use of signal analysis of heart sounds and murmurs to assess severity of mitral valve regurgitation attributable to myxomatous mitral valve disease in dogs
Swedish University of Agriculture Science.
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
Swedish University of Agriculture Science.
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
Show others and affiliations
2009 (English)In: AMERICAN JOURNAL OF VETERINARY RESEARCH, ISSN 0002-9645 , Vol. 70, no 5, 604-613 p.Article in journal (Refereed) Published
Abstract [en]

Objective-To investigate use of signal analysis of heart sounds and murmurs in assessing severity of mitral valve regurgitation (mitral regurgitation [MR]) in dogs with myxomatous mitral valve disease (MMVD).

Animals-77 client-owned dogs.

Procedures-Cardiac sounds were recorded from dogs evaluated by use of auscultatory and echocardiographic classification systems. Signal analysis techniques were developed to extract 7 sound variables (first frequency peak, murmur energy ratio, murmur duration > 200 Hz, sample entropy and first minimum of the auto mutual information function of the murmurs, and energy ratios of the first heart sound [S1] and second heart sound [S2]).

Results-Significant associations were detected between severity of MR and all sound variables, except the energy ratio of S1. An increase in severity of MR resulted in greater contribution of higher frequencies, increased signal irregularity, and decreased energy ratio of S2. The optimal combination of variables for distinguishing dogs with high-intensity murmurs from other dogs was energy ratio of S2 and murmur duration > 200 Hz (sensitivity, 79%; specificity, 71%) by use of the auscultatory classification. By use of the echocardiographic classification, corresponding variables were auto mutual information, first frequency peak, and energy ratio of S2 (sensitivity, 88%; specificity, 82%).

Conclusions and Clinical Relevance-Most of the investigated sound variables were significantly associated with severity of MR, which indicated a powerful diagnostic potential for monitoring MMVD. Signal analysis techniques could be valuable for clinicians when performing risk assessment or determining whether special care and more extensive examinations are required.

Place, publisher, year, edition, pages
2009. Vol. 70, no 5, 604-613 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-18291DOI: 10.2460/ajvr.70.5.604OAI: oai:DiVA.org:liu-18291DiVA: diva2:217866
Available from: 2009-05-17 Created: 2009-05-15 Last updated: 2009-06-08

Open Access in DiVA

No full text

Other links

Publisher's full textLink to Journal

Authority records BETA

Ahlström, ChristerHult, PeterAsk, Per

Search in DiVA

By author/editor
Ahlström, ChristerHult, PeterAsk, Per
By organisation
Physiological MeasurementsThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 518 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf