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.
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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
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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
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