A personalized Gaussian mixture model modeling method for heart sound segmentation

Heart sound segmentation is a prerequisite for heart sound processing systems. Most methods utilize a supervised learning framework to construct the statistical model for heart sound segmentation,the major hindrancesof which are the hard work of hand-labeling training data and the mismatch between the training and testing datasets.The heart sound segmentation method based on a personalized statistical model is proposed, which does not rely on training data and enables the good match between the training and testing data. Heart sound signal is stationary in a short period. The Personalized Modeling Method(PMM) is based on an assumption that that the periodicity of heart sound signal is stationary in an analysis window which contains several cycles. The eigen signal for the cardiac cycle is extracted by making use of principal component analysis, based on which, the personalized model is constructed by an unsupervised learning framework. The heart sound signal is eventually segmented using the unsupervised model.Experiments showed that the proposed method outperforms the widely used LRHSMM by 3% in accuracy.