Abstract
This paper focuses on the study of detecting low frequency vibrations from the human chest and correlate them to cardiac conditions using new devices and techniques, custom software, and the Support Vector Machine (SVM) classification technique. Several new devices and techniques of detecting a human heart murmur have been developed through the extraction of vibrations primarily in the range of 10 – 150 Hertz (Hz) on the human chest. The devices and techniques have been tested on different types of simulators and through clinical trials. Signals were collected using a Kardiac Infrasound Device (KID) and accelerometers integrated with a custom MATLAB software interface and a data acquisition system. Using the interface, the data was analyzed and classified by an SVM approach. Results show that the SVM was able to classify signals under different testing environments. For clinical trials, the SVM distinguished between normal and abnormal cardiac conditions and between pathological and non-pathological cardiac conditions. Finally, using the various devices, a correlation between heart murmurs and normal hearts was observed from human chest vibrations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Epstein, O., et al.: Clinical Examination. Gower Medical Publishing, New York (1992)
Dr. Computer Check for Dangerous Heart Murmurs. Prevention 54(1), 112 (2002)
Watrous, et al.: Computer-Assisted Detection of Systolic Murmurs Associated with Hypertrophy Cardiomyopathy. Texas Heart Institute Journal 31(4), 368 (2004)
Mangione, S., et al.: The Teaching and Practice of Cardiac Auscultation during Internal Medicine and Cardiology Training. Annals of Internal Medicine 119, 47–54 (1993)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks 10, 988–999 (1999)
Gunn, S.R.: Support Vector Machine for Classification and Regression. Technical Report, University of Southampton, Southampton, UK (1998)
Rud, S., et al.: Non-Invasive Infrasound Heart Murmur Detection. Senior Project Report, Department of Electrical and Computer Engineering. University of Minnesota, Duluth (2005)
Haykin, S.: Neural Networks – A Comprehensive Foundation. Prentice Hall, New York (1999)
Pelckmans, K., et al.: LS-SVM Toolbox User’s Guide, Version 1.4. Department of Electrical Engineering, Katholieke Universiteit Leuven (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rud, S., Yang, JS. (2010). A Support Vector Machine (SVM) Classification Approach to Heart Murmur Detection. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)