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Feature Extraction for Systolic Heart Murmur Classification
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
Örebro university.
(Department of Internal Medicine, County Hospital Ryhov, Jönköping, Sweden)
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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.

Place, publisher, year, edition, pages
2006. Vol. 34, no 11, 1666-1677 p.
Keyword [en]
Auscultation, Bioacoustics, Feature selection, Heart sounds, Valvular disease
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13044DOI: 10.1007/s10439-006-9187-4OAI: oai:DiVA.org:liu-13044DiVA: diva2:17717
Available from: 2008-03-20 Created: 2008-03-20 Last updated: 2017-12-13
In thesis
1. Nonlinear phonocardiographic Signal Processing
Open this publication in new window or tab >>Nonlinear phonocardiographic Signal Processing
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
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:nbn:se:liu:diva-11302 (URN)978-91-7393-947-8 (ISBN)
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
2. Processing of the Phonocardiographic Signal: methods for the intelligent stethoscope
Open this publication in new window or tab >>Processing of the Phonocardiographic Signal: methods for the intelligent stethoscope
2006 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Phonocardiographic signals contain bioacoustic information reflecting the operation of the heart. Normally there are two heart sounds, and additional sounds indicate disease. If a third heart sound is present it could be a sign of heart failure whereas a murmur indicates defective valves or an orifice in the septal wall. The primary aim of this thesis is to use signal processing tools to improve the diagnostic value of this information. More specifically, three different methods have been developed:

• A nonlinear change detection method has been applied to automatically detect heart sounds. The first and the second heart sounds can be found using recurrence times of the first kind while the third heart sound can be found using recurrence times of the second kind. Most third heart sound occurrences were detected (98 %), but the amount of false extra detections was rather high (7 % of the heart cycles).

• Heart sounds obscure the interpretation of lung sounds. A new method based on nonlinear prediction has been developed to remove this undesired disturbance. High similarity was obtained when comparing actual lung sounds with lung sounds after removal of heart sounds.

• Analysis methods such as Shannon energy, wavelets and recurrence quantification analysis were used to extract information from the phonocardiographic signal. The most prominent features, determined by a feature selection method, were used to create a new feature set for heart murmur classification. The classification result was 86 % when separating patients with aortic stenosis, mitral insufficiency and physiological murmurs.

The derived methods give reasonable results, and they all provide a step forward in the quest for an intelligent stethoscope, a universal phonocardiography tool able to enhance auscultation by improving sound quality, emphasizing abnormal events in the heart cycle and distinguishing different heart murmurs.

Place, publisher, year, edition, pages
Institutionen för medicinsk teknik, 2006. 75 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1253
Keyword
Bioacoustics, phonocardiographic, signal processing, heart sound, lung sound, nonlinear dynamics
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-7538 (URN)LiU-TEK-LIC-2006:34 (Local ID)91-85523-59-3 (ISBN)LiU-TEK-LIC-2006:34 (Archive number)LiU-TEK-LIC-2006:34 (OAI)
Presentation
2006-05-31, IMT1, Campus US, Linköpings universitet, Linköping, 00:00 (English)
Opponent
Supervisors
Available from: 2006-10-09 Created: 2006-10-09 Last updated: 2010-01-14Bibliographically approved

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Ahlström, ChristerHult, PeterNylander, EvaDahlström, UlfAsk, Per

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