Heart Murmur Recognition Based on Hidden Markov Model  

作　　者：Lisha Zhong Jiangzhong Wan Zhiwei Huang Gaofei Cao Bo Xiao 

机构地区：[1]Department of Biomedical Engineering, Luzhou Medical College, Luzhou, China [2]Department of Biomedical Engineering, Luzhou Medical College, Luzhou, China.

出　　处：《Journal of Signal and Information Processing》2013年第2期140-144,共5页信号与信息处理（英文）

摘　　要：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.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.

关 键 词：HEART MURMUR WAVELET Threshold DE-NOISING Mel Frequency CEPSTRUM Hidden MARKOV Model 

分 类 号：R73[医药卫生—肿瘤]