nach oben

Medical & Biological Engineering & Computing

Erschienen in:

01.07.2012 | Original Article

Signal feature extraction by multi-scale PCA and its application to respiratory sound classification

verfasst von: Shengkun Xie, Feng Jin, Sridhar Krishnan, Farook Sattar

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 7/2012

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34 % for the classification of 689 real RS recording segments shows the promising performance of the presented method.

Vorheriger Artikel Accuracy and efficacy of thoracic pedicle screws in scoliosis with patient-specific drill template

Nächster Artikel High-resolution esophageal long-term ECG allows detailed atrial wave morphology analysis in case of atrial ectopic beats

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Al-Naami B, Al-Nabulsi J, Amasha H, Torry J (2010) Utilizing wavelet transform and support vector machine for detection of the paradoxical splitting in the second heart sound. Med Biol Eng Comput 48:177-184PubMedCrossRef

Aminghafari M, Cheze N, Poggi JM (2006) Multivariate de-noising using wavelets and principal component analysis. Comput Stat Data Anal 50:2381-2398CrossRef

Bahoura M (2009) Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Comput Biol Med 39:824-843PubMedCrossRef

Bahoura M, Pelletier C (2003) New parameters for respiratory sound classification. In: Proceedings of IEEE Conference Electrical Computer Engineering, vol 3, pp 1457–1460

Bakshi B (1998) Multiscale pca with application to mspc monitoring. AIChE J 44:1596–1610CrossRef

Bakshi B (1999) Multiscale analysis and modeling using wavelets. J Chemometrics 13:34, 415–434

Chien JC, Wu HD, Chong FC, Li CI (2007) Wheeze detection using cepstral analysis in gaussian mixture models. In: Proceedings of 29th IEEE EMBS Conf, pp 3168–3171

Chitaliya NG, Trivedi AI (2010) Feature extraction using wavelet-pca and neural network for application of object classification and face recognition. In: Second International Conference on Computer Engineering and Applications (ICCEA), vol 1, pp 510–514

Folland R, Hines E, Boilot P, Morgan D (2002) Classifying coronary dysfunction using neural networks through cardiovascular auscultation. Med Biol Eng Comput 40:339–343PubMedCrossRef

10.

Forgacs P (1978) The functional basis of pulmonary sounds. Chest 73(3):399PubMedCrossRef

11.

Fredberg JJ, Holford SK (1983) Crackles: recording, analysis and clinical significance. J Acoust Soc Am 73:1036–1046PubMedCrossRef

12.

Homs-Corbera A, Fiz JA, Morera J, Jané R (2005) Time–frequency detection and analysis of wheezes during forced exhalation. IEEE Trans Biomed Eng 51(1):182–186CrossRef

13.

Jin F, Krishnan S, Sattar F (2011) Adventitious sounds identification and extraction using temporal-spectral dominance based features. IEEE Trans Biomed Eng (in press)

14.

Kandaswamy A, Kumar CS, Ramanathan R, Jayaramana S, Malmurugan N (2004) Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 34(6):523–537PubMedCrossRef

15.

Lehrer S (1993) Understanding lung sounds. Saunders, Philadelphia

16.

Mazloom M, Kasaei S, Neissi HA (2009) Construction and application of svm model and waveletpca for face recognition. In: Second International Conference on Computer and Electrical Engineering, 2009. ICCEE ’09. vol 1, pp 391–398

17.

Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 6(2):559–572

18.

Percival DP, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, Cambridge

19.

Piirilä P, Sovijärvi ARA (1995) Crackles: recording, analysis and clinical significance. Eur Respir J 8:2139–2148PubMedCrossRef

20.

Safari M, Harandi MT, Araabi BN (2004) A svm based method for face recognition using a wavelet pca representation of faces. In: International conference on image processing, 2004. ICIP’04, vol 2, pp 853–856

21.

Sankur B, Kahya YP, Guler EC, Engin T (1994) Comparison of AR-based algorithms for respiratory sounds classification. Comput Biol Med 24(1):67–76PubMedCrossRef

22.

Sovijärvi ARA, Vanderschoot J, Eavis JR (2000) Standardization of computerized respiratory sound analysis. Eur Respir Rev 10(77):585–649

23.

Taplidou SA, Hadjileontiadis LJ (2007) Wheeze detection based on time–frequency analysis of breath sounds. Comput Biol Med 37:1073–1083PubMedCrossRef

24.

Tilkian AG, Conover MB (2001) Understanding heart sounds and murmurs: with an introduction to lung sounds. Philadelphia

25.

Vidakovic B (1999) Statistical modeling by wavelets. Wiley, New York

26.

Wang W, Guo Z, Yang J, Zhang Y, Durand L, Loew M (2001) Analysis of the first heart sound using the matching pursuit method. Med Biol Eng Comput 39:644–648PubMedCrossRef

27.

Wilkins RL, Hodgkin JE, Lopez B (2004) Fundamentals of lung and heart sounds. Audio CD, Mosby

28.

Xie S, Lio P, Lawniczak AT (2009) A comparative study of noise effect on wavelet based de-nosing methods. In: Proceedings of IEEE TIC-STH, pp 991–999

Titel: Signal feature extraction by multi-scale PCA and its application to respiratory sound classification
verfasst von: Shengkun Xie
Feng Jin
Sridhar Krishnan
Farook Sattar
Publikationsdatum: 01.07.2012
Verlag: Springer-Verlag
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 7/2012
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-012-0903-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 7/2012

Where does transcranial magnetic stimulation (TMS) stimulate? Modelling of induced field maps for some common cortical and cerebellar targets

Experimental comparison of connectivity measures with simulated EEG signals

A CAD/CAM-prototyped anatomical condylar prosthesis connected to a custom-made bone plate to support a fibula free flap

High-resolution esophageal long-term ECG allows detailed atrial wave morphology analysis in case of atrial ectopic beats

Modeling the relationship between Higuchi’s fractal dimension and Fourier spectra of physiological signals

Relation of fetal heart rate signals with unassignable baseline to poor neonatal state at birth

Premium Partner