title: [Research on biometric method of heart sound signal based on GMM].
abstract: OBJECTIVE
  Extraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal.
  
  
  METHODS
  Firstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm.
  
  
  RESULTS
  The comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM.
  
  
  CONCLUSIONS
  Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.
authors.affiliation: Basic Medical College, Lu Zhou Medical College, Luzhou, 646000.
authors.forename: Lisha
authors.initials: L
authors.lastname: Zhong
authors.forename: Jiangzhong
authors.initials: J
authors.lastname: Wan
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Huang
authors.forename: Xingming
authors.initials: X
authors.lastname: Guo
authors.forename: Yun
authors.initials: Y
authors.lastname: Duan
mesh_terms: D000465:Algorithms; D001699:Biometry; D006321:Heart / Q000502:physiology; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D010701:Phonocardiography / Q000379:methods*; D058067:Wavelet Analysis