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Heart Murmur Recognition Based on Hidden Markov Model

Author(s): Lisha Zhong | Jiangzhong Wan | Zhiwei Huang | Gaofei Cao | Bo Xiao

Journal: Journal of Signal and Information Processing
ISSN 2159-4465

Volume: 04;
Issue: 02;
Start page: 140;
Date: 2013;
Original page

Keywords: Heart Murmur | Wavelet Threshold De-Noising | Mel Frequency Cepstrum | Hidden Markov Model

Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method is able to recognize the murmur efficiently and superior to BP neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.
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