基于GMM的心音信号生物识别方法研究  

Research on Biometric Method of Heart Sound Signal Based on GMM

作　　者：钟丽莎[1] 万江中[1] 黄志伟[1] 郭兴明[2] 段赟[3] 

机构地区：[1]泸州医学院生物医学工程系,泸州市646000 [2]重庆大学生物工程学院,重庆市400044 [3]第四军医大学唐都医院,西安市710000

出　　处：《中国医疗器械杂志》2013年第2期92-95,99,共5页Chinese Journal of Medical Instrumentation

基　　金：国家自然科学基金资助项目(30770551);四川省教育厅资助项目(201147)

摘　　要：目的将倒谱系数提取和高斯混合模型(GMM)相结合,提出了一种基于心音信号的生物识别方法。方法首先心音信号预处理小波去噪,然后进行特征参数的选择,对比研究了线性预测倒谱系数(LPCC)和Mel频率倒谱系数(MFCC),再用高斯混合模型(GMM)进行识别。最后利用50名志愿者的100段心音信号对所提出的方法进行验证。结果对比实验证明LPCC比MFCC更适合用于心音信号的生物识别研究,通过对每段心音信号进行小波去噪,取得了比传统GMM方法更高的识别率。结论表明该方法能够有效提高系统的识别性能,达到了比较理想的识别效果。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. Conclusion Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

关 键 词：心音信号 生物识别 高斯混合模型 小波去噪 线性预测倒谱系数 MEL频率倒谱系数 

分 类 号：R318[医药卫生—生物医学工程]