Abstract
This article deals with the challenging problem of segmenting narrowly spaced cardiac events (S1 and S2) in noisy phonocardiogram (PCG) signals by using a novel application of NMF based on time-scale approach. A novel energy-based method is proposed for the segmentation of noisy PCG signals in order to detect cardiac events, which could be closely spaced and separated by noisy gaps. The method is based on time-scale transform as well as nonnegative matrix factorization (NMF) and the segmentation problem is formulated using the paradigm of binary statistical hypothesis testing. The energy of the Morlet wavelet transform and NMF output is employed as a test statistics for segmentation where the number of scales are selected based on the preferences calculated along the time-scales. The simulation results using real recorded noisy PCG data that provide promising performance with high overall accuracy on the segmentation of narrowly separated, high noisy signals by our proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A. Moukadem, A. Dieterlen, N. Hueber, C. Brandt, Comparative study of heart sounds localization, bioelectronics, biomedical and bio-inspired systems. SPIE N 8068A-27, Prague (2011)
C.S. Lima, A. Tavares, J. Correia, M.J. Cardoso, D. Barbosa, in New Developments in Biomedical Engineering, ed. by Domenico Campolo, ISBN 978-953-7619-57-2 (InTech Publisher), p. 37–72 (2010)
H. Liang, S. Lukkarinen, I. Hartimo, Heart Sound Segmentation Algorithm Based on Heart Sound Envelogram (Helsinki University of Technology, Espoo, 1997)
C. Ahlström, Nonlinear Phonocardiographic Signal Processing. Institutionen för Medicinsk Teknik, 2008
H. Naseri, M.R. Homaeinezhad, H. Pourkhajeh, Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval. Comput. Biol. Med. 43, 1205–1213 (2013)
S. Ari, K. Hembram, G. Saha, Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier. Expert Syst. Appl. 37(12), 8019–8026 (2010)
H. Hadi, M. Mashor, M. Suboh, M. Mohamed, Classification of heart sound based on S-transform and neural networks, in International Conference on Information Science, Signal Processing and Their Applications (2010)
D.D. Lee, H.S. Seung, Algorithms for non-negative matrix factorization. Adv. Neural Inf. Proc. Syst. 13, 556–562 (2001)
Y.-X. Wang, Y.-J. Zhang, Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25(6), 1336–1353 (2013)
A.K. Kattepur, F. Jin, F. Sattar, Single channel source separation for convolutive mixture with application to respiratory sounds, in IEEE-EMBS Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS) (2010)
B. Ghoraani, S. Krishnan, Time-frequency matrix feature extraction and classification of environmental audio signals. IEEE Trans. Audio Speech Lang. Process. 19(7), 2197–2209 (2011)
M. Kim, J. Yoo, K. Kang, S. Choi, Nonnegative matrix partial co-factorization for spectral and temporal drum source separation. IEEE J. Sel. Top. Signal Process. 5(6), 1192–1204 (2011)
G. Shah, P. Koch, C.B. Papadias, On the blind recovery of cardiac and respiratory sounds. IEEE J. Biomed. Health Inform. 19(1), 151–157 (2015)
P.O. Hoyer, Non-negative sparse coding, in IEEE Workshop Neural Networks for Signal Process (2002), pp. 557–565
J. Eggert, E. Kmrner, Sparse coding and NMF, in IEEE Conference on Neural Networks, vol. 4 (2004), pp. 2529–2533
C.J. Lin, Projected gradient methods for non negative matrix factorization. Neural Comput. 19(10), 2756–2779 (2007)
D. Kim, S. Sra, and I.S. Dhillon, Fast newton-type methods for the least squares non negative matrix approximation problem, in SIAM Conference on Data Mining (2007)
C. Torrence, G.P. Compo, A practical guide to wavelet analysis. Bull. Am. Meterol. Soc. 79(1), 61–76 (1998)
L.L. Scharf, Statistical Signal Processing: Detection Estimation and Time Series Analysis (Addison-Wesley, Boston, 1991)
S.M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory (Prentice Hall, Englewood Cliffs, 1998)
N. Lee, Q. Huynh, S. Schwartz, New methods of linear time-frequency analysis for signal detection, in IEEE Proceedings on Time-Frequency and Time-Scale Analysis, Paris, June 1996, pp. 13–16
A.-J. Van der Veen, E.D.F. Deprettere, A.L. Swindlehurst, Sub-space based signal analysis using singular value decomposition. Proc. IEEE 81(9), 1277–1307 (1993)
B. Huang, A. Kunoth, An optimization based empirical mode decomposition scheme. J. Comput. Appl. Math. 240(2013), 174–183 (2013)
A. Moukadem, A. Dieterlen, N. Hueber, C. Brandt, A robust heart sounds segmentation module based on S-transform. Biomed. Signal Process. Control 8, 273–281 (2013)
S. Ventosa, C. Simon, M. Schimmel, J. Danobeitia, A. Manuel, The S-transform from a wavelet point of view. IEEE Trans. Signal Process. 56(7), 2771–2780 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sattar, F., Jin, F., Moukadem, A., Brandt, C., Dieterlen, A. (2016). Time-Scale-Based Segmentation for Degraded PCG Signals Using NMF. In: Naik, G. (eds) Non-negative Matrix Factorization Techniques. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48331-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-662-48331-2_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48330-5
Online ISBN: 978-3-662-48331-2
eBook Packages: EngineeringEngineering (R0)