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
Nonlinear phonocardiographic Signal Processing
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Health Sciences.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of this thesis work has been to develop signal analysis methods for a computerized cardiac auscultation system, the intelligent stethoscope. In particular, the work focuses on classification and interpretation of features derived from the phonocardiographic (PCG) signal by using advanced signal processing techniques.

The PCG signal is traditionally analyzed and characterized by morphological properties in the time domain, by spectral properties in the frequency domain or by nonstationary properties in a joint time-frequency domain. The main contribution of this thesis has been to introduce nonlinear analysis techniques based on dynamical systems theory to extract more information from the PCG signal. Especially, Takens' delay embedding theorem has been used to reconstruct the underlying system's state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction. In this thesis, the PCG signal's structure in state space has been exploited in several applications. Change detection based on recurrence time statistics was used in combination with nonlinear prediction to remove obscuring heart sounds from lung sound recordings in healthy test subjects. Sample entropy and mutual information were used to assess the severity of aortic stenosis (AS) as well as mitral insufficiency (MI) in dogs. A large number of, partly nonlinear, features was extracted and used for distinguishing innocent murmurs from murmurs caused by AS or MI in patients with probable valve disease. Finally, novel work related to very accurate localization of the first heart sound by means of ECG-gated ensemble averaging was conducted. In general, the presented nonlinear processing techniques have shown considerably improved results in comparison with other PCG based techniques.

In modern health care, auscultation has found its main role in primary or in home health care, when deciding if special care and more extensive examinations are required. Making a decision based on auscultation is however difficult, why a simple tool able to screen and assess murmurs would be both time- and cost-saving while relieving many patients from needless anxiety. In the emerging field of telemedicine and home care, an intelligent stethoscope with decision support abilities would be of great value.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik , 2008. , 213 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1168
Keyword [en]
Signal analysis methods, computerized cardiac auscultation system, phonocardiographic (PCG) signal, mitral insufficiency (MI), time- and cost-saving
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-11302ISBN: 978-91-7393-947-8 (print)OAI: oai:DiVA.org:liu-11302DiVA: diva2:17719
Public defence
2008-04-25, Elsa Brändströmsalen, Universitetssjukhuset, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2009-04-21
List of papers
1. A method for accurate localization of the first heart sound and possible applications
Open this publication in new window or tab >>A method for accurate localization of the first heart sound and possible applications
2008 (English)In: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 29, no 3, 417-428 p.Article in journal (Refereed) Published
Abstract [en]

We have previously developed a method for localization of the first heart sound (S1) using wavelet denoising and ECG-gated peak-picking. In this study, an additional enhancement step based on cross-correlation and ECG-gated ensemble averaging (EA) is presented. The main objective of the improved method was to localize S1 with very high temporal accuracy in (pseudo-) real time. The performance of S1 detection and localization, with and without EA enhancement, was evaluated on simulated as well as experimental data. The simulation study showed that EA enhancement reduced the localization error considerably and that S1 could be accurately localized at much lower signal-to-noise ratios. The experimental data were taken from ten healthy subjects at rest and during invoked hyper- and hypotension. For this material, the number of correct S1 detections increased from 91% to 98% when using EA enhancement. Improved performance was also demonstrated when EA enhancement was used for continuous tracking of blood pressure changes and for respiration monitoring via the electromechanical activation time. These are two typical applications where accurate localization of S1 is essential for the results.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2008
Keyword
ensemble averaging, detection, localization, heart sound, bioacoustics
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-11856 (URN)10.1088/0967-3334/29/3/011 (DOI)
Note
Original publication: C Ahlstrom, T Länne, P Ask and A Johansson, A method for accurate localization of the first heart sound and possible applications, 2008, Physiological Measurement, (29), 3, 417-428. http://dx.doi.org/10.1088/0967-3334/29/3/011. Copyright: Institute of Physics and IOP Publishing Limited, http://www.iop.org/EJ/journal/PMAvailable from: 2008-05-20 Created: 2008-05-20 Last updated: 2017-12-13
2. Assessing Aortic Stenosis using Sample Entropy of the Phonocardiographic Signal in Dogs
Open this publication in new window or tab >>Assessing Aortic Stenosis using Sample Entropy of the Phonocardiographic Signal in Dogs
Show others...
2008 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 55, no 8, 2107-2109 p.Article in journal (Refereed) Published
Abstract [en]

In aortic valve stenosis (AS), heart murmurs arise as an effect of turbulent blood flow distal to the obstructed valves. With increasing AS severity, the flow becomes more unstable, and the ensuing murmur becomes more complex. We hypothesize that these hemodynamic flow changes can be quantified based on the complexity of the phonocardiographic (PCG) signal. In this study, sample entropy (SampEn) was investigated as a measure of complexity using a dog model. Twenty-seven boxer dogs with various degrees of AS were examined with Doppler echocardiography, and the peak aortic flow velocity (Vmax) was used as a reference of AS severity. SampEn correlated to Vmax with R = 0.70 using logarithmic regression. In a separate analysis, significant differences were found between physiologic murmurs and murmurs caused by AS (p < 0.05), and the area under a receiver operating characteristic curve was calculated to 0.96. Comparison with previously presented PCG measures for AS assessment showed improved performance when using SampEn, especially for differentiation between physiological murmurs and murmurs caused by mild AS. Studies in patients will be needed to properly assess the technique in humans.

Keyword
Aortic stenosis (AS), bioacoustics, heart sound, murmur, sample entropy (SampEn)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-13042 (URN)10.1109/TBME.2008.923767 (DOI)
Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2017-12-13
3. Assessing mitral regurgitation attributable to myxomatous mitral valve disease in dogs using signal analysis of heart sounds and murmurs
Open this publication in new window or tab >>Assessing mitral regurgitation attributable to myxomatous mitral valve disease in dogs using signal analysis of heart sounds and murmurs
Show others...
2008 (English)Article in journal (Refereed) Submitted
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-13043 (URN)
Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2009-03-26
4. Feature Extraction for Systolic Heart Murmur Classification
Open this publication in new window or tab >>Feature Extraction for Systolic Heart Murmur Classification
Show others...
2006 (English)In: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 34, no 11, 1666-1677 p.Article in journal (Refereed) Published
Abstract [en]

Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an “intelligent stethoscope” with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil’s sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

Keyword
Auscultation, Bioacoustics, Feature selection, Heart sounds, Valvular disease
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13044 (URN)10.1007/s10439-006-9187-4 (DOI)
Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2017-12-13
5. Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
Open this publication in new window or tab >>Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
2005 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, no 12, 812-815 p.Article in journal (Refereed) Published
Abstract [en]

Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2005
Keyword
Bioacoustics, heart sound (HS), lung sound (LS), nonlinear prediction, recurrence time statistics
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-11857 (URN)10.1109/LSP.2005.859528 (DOI)
Note
Original publication: Ahlstrom, C., Liljefeldt, O., Hult, P. and Ask, P., Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction, 2005, IEEE Signal Processing Letters, (12), 12, 812-815. http://dx.doi.org/10.1109/LSP.2005.859528. Copyright: IEEE, http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=97Available from: 2008-05-20 Created: 2008-05-20 Last updated: 2017-12-13

Open Access in DiVA

cover(254 kB)160 downloads
File information
File name COVER01.pdfFile size 254 kBChecksum MD5
7843a4e84c5ee5b07a85eeee8ed91efdbf7a3e89b7a05b40d8a458d6865f2ef4fec81241
Type coverMimetype application/pdf
fulltext(7527 kB)9739 downloads
File information
File name FULLTEXT01.pdfFile size 7527 kBChecksum MD5
870ab4510db4024c3e8c9f6c77df9c4dd82a05e874d67daf39a85ff13f54e1156dbe5588
Type fulltextMimetype application/pdf

Authority records BETA

Ahlström, Christer

Search in DiVA

By author/editor
Ahlström, Christer
By organisation
Physiological MeasurementsFaculty of Health Sciences
Medical Laboratory and Measurements Technologies

Search outside of DiVA

GoogleGoogle Scholar
Total: 9739 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 5478 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