ICT Research Center, Amir Kabir University, Tehran, Iran.
Comput Methods Programs Biomed. 2010 Jul;99(1):43-8. doi: 10.1016/j.cmpb.2009.10.006. Epub 2009 Dec 29.
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.
本文提出了一种新的儿科心音分段方法，特别关注呼吸对儿科心音的生理影响。该分段分三个步骤完成。首先，通过使用信号的短时谱能量和自回归（AR）参数，重点关注第一心音（S1）和第二心音（S2），获得心音信号的包络。然后，通过使用多层感知器（MLP）神经网络分类器，考虑 S1 和 S2 声音的重复和频谱特性，提取基本心音。在最后一步，考虑到由于儿童呼吸的影响而导致的舒张期和收缩期间隔变化，实现了完整而精确的心音端点和分段。
