Optimization of the Extraction Method of MFCC Feature Vectors for Heart Sound Signals

In order to improve the accuracy of heart sound signal classification using the Mel frequency cepstral coefficient (MFCC) feature vector, we have proposed an MFCC feature vector optimization method based on independent component analysis (ICA) and weight optimization. First, the heart sound signal is preprocessed by removing the trend terms, noise reduction, extracting the cardiac cycle and fundamental heart sounds segmentation, etc. Then, the MFCC feature vector is extracted by performing Mel spectrum transform and cepstrum analysis on each extracted fundamental heart sounds. In the process, the discrete cosine transform is replaced by ICA to remove the correlation of high-order quantities between components, and the correlation coefficient is used as the weight to optimize the overall mixing matrix. Finally, the F-ratio is used as the feature vector contribution rate, and each dimension feature vector is optimized with the weight. The first heart sound and the second heart sound are identified by the support vector machine classifier by extracting the MFCC feature vector, and compared with the artificially labeled heart sound states set. The experimental results show that the recognition rate of MFCC feature vector based on ICA and weight optimization is effectively improved in SVM classifier, and the optimization algorithm has certain anti-noise performance.