基于PCA串并融合的多路心音特征表征方法

多路心音信号不仅比单路心音信号涵盖更多关于总体的特征，而且能够弥补单路心音数据携带的信息量可能不充分的缺陷。利用笔者自主设计的4路心音传感器，初步建立一个小型4路心音数据库。基于这个数据库，首先阐明多路心音信号的特点，论述心杂音与听诊位置的关系；然后分别提取心音的单路和4路能量熵系数、4路心音互信息作为有效特征数据集，利用PCA对能量熵特征进行降维处理，获得串行特征；将相关性特征和互信息特征从实向量空间拓展到复向量空间，进行并行融合，获得并行特征；最后将串行并行特征再次融合成为多元优化组合特征。这种融合策略，具有针对性强，凸显差异性的优点。仿真实验结果表明，由多路心音信号获取的多元优化组合特征表征效果明显优于单路心音信号的特征表征效果，不仅有益于分类模型的构建，而且对实现先心病的快速筛查，提高分类识别率具有积极的意义。

Multi-channel Heart Sound Feature Characterization Method #br# Based on PCA Serial-merged Fusion

The multi-channel heart sound signal not only covers more general characteristics than the single-channel heart sound signals, but also can make up for the defect that the information carried by the single-channel heart sound data. Using a four-way heart sound sensor, a small four-way heart sound database was established. Based on it, firstly, the characteristics of multi-channel heart sound signals are clarified, and the relationship between heart murmur and auscultation position is discussed. Then, the single-channel and four-channel energy entropy are extracted as effective feature data by using PCA pair. The energy entropy feature is dimension-reduced to obtain serial features. The correlation features and mutual information features are extended from real vector space to complex vector space. Finally, serial parallel features are re-converged into multi-optimal combinations feature. The simulation results show that the feature representation of multivariate optimal combination obtained by multi-channel heart sound signals is better than that of single-channel heart sound signals, which is not only beneficial to the construction of classification models, but also to quickly screen for congenital heart disease and improve classification. The recognition rate has a positive meaning.