心音信号MFCC特征向量提取方法的优化

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

为了提高利用梅尔频率倒谱系数(Mel-frequency cepstral coefficients, MFCC)特征向量进行心音信号分类的准确率,本文提出以一种基于独立成分分析(independent component analysis, ICA)及权值优化的MFCC特征向量优化方法。首先,通过消除趋势项、降噪、提取心动周期与基础心音分割等步骤对心音信号预处理;接着,对提取的基础心音信号做Mel频谱变换及倒谱分析提取MFCC特征向量,其中用ICA替代离散余弦变换去除分量间高阶量的相关性,同时采用相关系数为权值优化整体混合矩阵;最后,采用F比衡量特征向量贡献率,并以其为权值优化各维特征向量。通过提取MFCC特征向量采用支持向量机(support vector machine, SVM)的分类器识别第一心音及第二心音,并与人工标注心音状态集进行对比。实验结果表明,基于ICA及权值优化的MFCC特征向量在SVM分类器中识别率得到了有效的提升,且优化算法具备一定抗噪性能。

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(SVM)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.