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
Severity assessments of aortic stenosis using intelligent phonocardiography
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Objectives: To study capabilities of the intelligent phonocardiography (IPCG) in automatic grading severity of the aortic stenosis (AS).

Methods: Phonocardiogram signals were recorded from the patients with AS, as diagnosed by echocardiography. The patient group is comprised of signals, recorded from 5 patients (2 recordings from each), mostly elderly referrals (>60 years) with mild to severe AS. An advanced processing algorithm, consisted of the wavelet transform and the stepwise regression analysis, characterizes the systolic murmur caused by the AS in order to predict the 5 indicators; mean pressure gradient over the aortic valve (MPG), maximum jet velocity (MJV), aortic valve area (AVA), velocity time integral and the ejection period. The automatic assessment is performed by an artificial neural network using the predicted values of the indicators as the input data. Reliability of the IPCG is validated by applying repeated random sub-sampling (RRSS) with 70%/30% of the training/testing data, and calculating the accuracy. The RRSS is also employed to validate reproducibility of the IPCG by using 70% of the signals for training and the second recording of the same individuals for  testing.

Results: Accuracy of the IPCG is estimated to be and (95% confidence interval) for the reliability and the reproducibility, respectively. Linear correlation between the characterized systolic murmur and the MPG (r>0.81), the MJV (r>0.82) and the AVA (r>0.85) is observed.

Conclusions: The IPCG has the potential to objectively serve as a clinical tool for grading severity of the aortic stenosis.

National Category
Biomedical Laboratory Science/Technology Medical Biotechnology
Identifiers
URN: urn:nbn:se:liu:diva-110181OAI: oai:DiVA.org:liu-110181DiVA: diva2:743380
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2014-09-04Bibliographically approved
In thesis
1. Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
Open this publication in new window or tab >>Assessment of Valvular Aortic Stenosis by Signal Analysis of the Phonocardiogram
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Aortic stenosis (AS) is one of the most prevalent valvular heart diseases in elderly people. According to the recommendations of both the American Heart Association and the European Society of Cardiology, severity assessment of AS is primarily based on echocardiographic findings. The experience of the investigator here play important roles in the accuracy of the assessment, and therefore in the disease management. However, access to the expert physicians could be limited, especially in rural health care centers of developing countries.

This thesis aims to develop processing algorithms tailored for phonocardiographic signal with the intension to obtain a noninvasive diagnostic tool for AS assessment and severity grading. The algorithms employ a phonocardiogram as input signal and perform analysis for screening and diagnostics. Such a decision support system, which we call “the intelligent phonocardiography”, can be widely used in primary healthcare centers.

The main contribution of the thesis is to present innovative models for the phonocardiographic analysis by taking the segmental characteristics of the signal into consideration. Three novel methodologies are described, based on the presented models, to perform robust classification. In the first attempt, a novel pattern recognition framework is presented for screening of AS-related murmurs. The framework offers a hybrid model for classifying cyclic time series in general, but is tailored to detect the murmurs as a special case study. The time growing neural network is another method that we use to classify short time signals with abrupt frequency transition. The idea of the growing frames is extended to the cyclic signals with stochastic properties for the screening purposes. Finally, a combined statistical and artificial intelligent classifier is proposed for grading the severity of AS.

The study suggests comprehensive statistical validations not only for the evaluation and representation of systolic murmurs but also for setting the methodology design parameters, which can be considered as one of the significant features of the study. The resulting methodologies can be implemented by using web and mobile technologies to be utilized in distributed healthcare system.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 81 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1616
National Category
Medical Biotechnology Medical Bioscience
Identifiers
urn:nbn:se:liu:diva-110182 (URN)978-91-7519-252-9 (ISBN)
Public defence
2014-09-26, Linden, Campus US, Linköpings universitet, Linköping, 10:00 (English)
Opponent
Supervisors
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2016-12-28Bibliographically approved

Open Access in DiVA

No full text

Authority records BETA

Gharehbaghi, ArashEkman, IngerAsk, PerNylander, EvaJanerot Sjöberg, Birgitta

Search in DiVA

By author/editor
Gharehbaghi, ArashEkman, IngerAsk, PerNylander, EvaJanerot Sjöberg, Birgitta
By organisation
Department of Biomedical EngineeringThe Institute of TechnologyDivision of Cardiovascular MedicineFaculty of Health SciencesDepartment of Clinical Physiology in LinköpingCenter for Medical Image Science and Visualization (CMIV)
Biomedical Laboratory Science/TechnologyMedical Biotechnology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 142 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