mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
A hybrid model for diagnosing sever aortic stenosis in asymptomatic patients using phonocardiogram
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Linköping University, Linköping, Sweden.
Linköping University, Linköping, Sweden.
Karolinska Institutet, Stockholm, Sweden.
Show others and affiliations
2015 (English)In: IFMBE Proceedings, 2015, Vol. 51, 1006-1009 p.Conference paper, Published paper (Refereed)
Abstract [en]

This study presents a screening algorithm for severe aortic stenosis (AS), based on a processing method for phonocardiographic (PCG) signal. The processing method employs a hybrid model, constituted of a hidden Markov model and support vector machine. The method benefits from a preprocessing phase for an enhanced learning. The performance of the method is statistically evaluated using PCG signals recorded from 50 individuals who were referred to the echocardiography lab at Linköping University hospital. All the individuals were diagnosed as having a degree of AS, from mild to severe, according to the echocardiographic measurements. The patient group consists of 26 individuals with severe AS, and the rest of the 24 patients comprise the control group. Performance of the method is statistically evaluated using repeated random sub sampling. Results showed a 95% confidence interval of (80.5%-82.8%) /(77.8%- 80.8%) for the accuracy/sensitivity, exhibiting an acceptable performance to be used as decision support system in the primary healthcare center.

Place, publisher, year, edition, pages
2015. Vol. 51, 1006-1009 p.
Keyword [en]
Aortic stenosis, Decision support, Hybrid model, Phonocardiogram, Primary healthcare centers, Algorithms, Artificial intelligence, Biomedical engineering, Blood vessels, Decision support systems, Diseases, Echocardiography, Health care, Hidden Markov models, Markov processes, Phonocardiography, Processing, Decision supports, Phonocardiograms, Primary healthcare, Diagnosis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-29381DOI: 10.1007/978-3-319-19387-8_245ISI: 000381813000245Scopus ID: 2-s2.0-84944326372ISBN: 9783319193878 (print)OAI: oai:DiVA.org:mdh-29381DiVA: diva2:862769
Conference
World Congress on Medical Physics and Biomedical Engineering, 2015, 7 June 2015 through 12 June 2015
Available from: 2015-10-23 Created: 2015-10-23 Last updated: 2016-12-27Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Lindén, Maria

Search in DiVA

By author/editor
Gharehbaghi, ArashLindén, Maria
By organisation
Embedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 18 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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