CAJ | 학술논문

针对心音信号非线性、非平稳的特性，提出一种基于经验模式分解(EMD)和关联维数的心音特征提取方法。首先通过EMD方法将心音信号分解成若干个固有模态函数(IMF)，并利用互相关系数准则对IMF进行筛选，结合G-P算法对主IMF(IMF1~IMF4)分量分别求其关联维数，以此作为神经网络的输入向量，实现了对正常心音信号和病理心音信号的分类识别。对于重构相空间中的两个重要参数时间延迟τ和关联维数m，分别采用互信息函数法和用Cao算法确定。对临床采集的心音数据按该方法进行测试，结果表明，该方法能有效地识别心音。
침대심음신호비선성、비평은적특성，제출일충기우경험모식분해(EMD)화관련유수적심음특정제취방법。수선통과EMD방법장심음신호분해성약간개고유모태함수(IMF)，병이용호상관계수준칙대IMF진행사선，결합G-P산법대주IMF(IMF1~IMF4)분량분별구기관련유수，이차작위신경망락적수입향량，실현료대정상심음신호화병리심음신호적분류식별。대우중구상공간중적량개중요삼수시간연지τ화관련유수m，분별채용호신식함수법화용Cao산법학정。대림상채집적심음수거안해방법진행측시，결과표명，해방법능유효지식별심음。
Focusing on the non-stationary and non-linear of heart sounds, a new method of feature extraction based on empirical mode decomposition (EMD) and Correlation dimension is proposed. The heart sound signals are decomposed into a finite number of intrinsic mode functions (IMFs). The IMF components are chosen by using the criteria of mutual correlation coefficient between IMF components and original signal and then the correlation dimension of the top four intrinsic mode functions (IMF1~IMF4) is calculated by using G-P algorithm. The eigenvectors are put into the artificial neural network for automatic discrimination between normal and abnormal signals. In the process of phase-space reconstruction, Cao theory and the mutual information function are used to determine the two important parameters: delay time and embedding dimension. The clinical data experimental diagnosis and contract test results show that the approach proposed could identify the pathological heart sound effectively.