A novel method for pediatric heart sound segmentation without using the ECG.

In this paper, we propose a novel method for pediatric heart sounds segmentation by paying special attention to the physiological effects of respiration on pediatric heart sounds. The segmentation is accomplished in three steps. First, the envelope of a heart sounds signal is obtained with emphasis on the first heart sound (S(1)) and the second heart sound (S(2)) by using short time spectral energy and autoregressive (AR) parameters of the signal. Then, the basic heart sounds are extracted taking into account the repetitive and spectral characteristics of S(1) and S(2) sounds by using a Multi-Layer Perceptron (MLP) neural network classifier. In the final step, by considering the diastolic and systolic intervals variations due to the effect of a child's respiration, a complete and precise heart sounds end-pointing and segmentation is achieved.