Feature extraction and choice in PCG based on Hilbert Transfer

In this paper, the key features of Phonocardiogram (PCG) are extracted based on the slopes of envelop of Hilbert Transfer after relocating boundaries with energy envelope segmentation. In this attempt the overall accuracy of features extraction is found to be 91.95%. 25 significant clinical features are introduced, and chosen to make two-kind classification by SVM. In the results of two-kind classification, the overall accuracy is 91.3%, which is better than 85.23% accuracy in 100 features of Shannon Energy Envelope. The result shows that features including clinical signification is of signification for enhancing the accurate rate of Phonocardiogram classification.