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Processing of the Phonocardiographic Signal: methods for the intelligent stethoscope
Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska högskolan.
2006 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Institutionen för medicinsk teknik , 2006. , s. 75
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1253
Emneord [en]
Bioacoustics, phonocardiographic, signal processing, heart sound, lung sound, nonlinear dynamics
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-7538Lokal ID: LiU-TEK-LIC-2006:34ISBN: 91-85523-59-3 (tryckt)OAI: oai:DiVA.org:liu-7538DiVA, id: diva2:22548
Presentation
2006-05-31, IMT1, Campus US, Linköpings universitet, Linköping, 00:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2006-10-09 Laget: 2006-10-09 Sist oppdatert: 2010-01-14bibliografisk kontrollert
Delarbeid
1. Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
Åpne denne publikasjonen i ny fane eller vindu >>Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
2005 (engelsk)Inngår i: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, nr 12, s. 812-815Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Institutionen för medicinsk teknik, 2005
Emneord
Bioacoustics, heart sound (HS), lung sound (LS), nonlinear prediction, recurrence time statistics
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-11857 (URN)10.1109/LSP.2005.859528 (DOI)
Merknad
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=97Tilgjengelig fra: 2008-05-20 Laget: 2008-05-20 Sist oppdatert: 2017-12-13
2. Detection of the 3rd Heart Sound using Recurrence Time Statistics
Åpne denne publikasjonen i ny fane eller vindu >>Detection of the 3rd Heart Sound using Recurrence Time Statistics
2006 (engelsk)Inngår i: Proc. 31st IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Toulouse, France, 2006, 2006, s. 1040-1043Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

The 3rd heart sound (S3) is normally heard during auscultation of younger individuals, but it is also common in many patients with heart failure. Compared to the 1st and 2nd heart sounds, S3 has low amplitude and low frequency content, making it hard to detect (both manually for the physician and automatically by a detection algorithm). We present an algorithm based on a recurrence time statistic which is sensitive to changes in a reconstructed state space, particularly for detection of transitions with very low energy. Heart sound signals from ten children were used in this study. Most S3 occurrences were detected (98 %), but the amount of false extra detections was rather high (7% of the heart cycles). In conclusion, the method seems capable of detecting S3 with high accuracy and robustness.

Serie
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
Emneord
acoustic, signal detection, bioacoustics, signal reconstruction, statistics, heart sound, auscultation, heart failure, reconstructed state space, recurrence time statistics
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-14058 (URN)
Tilgjengelig fra: 2006-10-09 Laget: 2006-10-09 Sist oppdatert: 2009-04-21
3. Feature Extraction for Systolic Heart Murmur Classification
Åpne denne publikasjonen i ny fane eller vindu >>Feature Extraction for Systolic Heart Murmur Classification
Vise andre…
2006 (engelsk)Inngår i: Annals of Biomedical Engineering, ISSN 0090-6964, E-ISSN 1573-9686, Vol. 34, nr 11, s. 1666-1677Artikkel i tidsskrift (Fagfellevurdert) 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.

Emneord
Auscultation, Bioacoustics, Feature selection, Heart sounds, Valvular disease
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-13044 (URN)10.1007/s10439-006-9187-4 (DOI)
Tilgjengelig fra: 2008-03-20 Laget: 2008-03-20 Sist oppdatert: 2017-12-13

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