A novel feature generation method based on nonlinear signal decomposition for automatic heart sound monitoring

This work presents a novel feature generation method for automatic heart sound monitoring system based on the nonlinear signal decomposition and the instantaneous characteristics of the decomposed components. In this work, first, the heart sounds (normal and abnormal) are decomposed by complementary ensemble empirical mode decomposition (CEEMD). Next, first five subcomponents are chosen empirically for further process. The instantaneous characteristics including instantaneous energy (IE) and frequency (IF) are estimated using Teager energy operator (TEO). After that, disregarding the energy and frequency information, total five IE versus IF maps are constructed. Then, the five IE-IF values transferred into a single feature space and using K-means algorithm, five mean values are selected. Further, a code book is constructed by vector quantization (VQ) method for the learning and future reference purpose. The experiment is performed on total 23 different classes of heart sounds including the normal and abnormal cases, collected from the Michigan Heart Sound and Murmur Database. The results indicate that the proposed method can achieve a recognition rate of 98%. Furthermore, a comparison with previous methods reveals that the proposed approach is superior. In contrast, the method is totally independent of any prior assumptions.