Application of EMD and Correlation Dimension in Classification and Recognition of Heart Sound

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.