Sava H P, Grant P M, McDonnell J T
Department of Electrical Engineering, University of Edinburgh, U.K.
IEEE Trans Biomed Eng. 1996 Oct;43(10):1046-8. doi: 10.1109/10.536906.
This paper demonstrates an improvement in the performance of spectral phonocardiography, combined with pattern recognition techniques for monitoring the condition of bioprosthetic heart valves. The analysis of the heart sounds is performed using a modified forward-backward overdetermined Prony's method. Results show that the condition of the bioprosthesis affects mostly the higher part of the spectrum (i.e., above 250 Hz) where no frequency components were found for malfunctioning cases. Therefore, the amplitudes of the three highest frequency components are used as the input vector of an adaptive single layer perceptron-based classifier to identify normal and malfunctioning classes. For the sample set examined, this method gives 100% correct discrimination between normal and malfunctioning Carpentier-Edwards (C-E) valves.
本文展示了频谱心音图性能的改进，结合模式识别技术用于监测生物人工心脏瓣膜的状况。心音分析采用改进的前后超定 Prony 方法进行。结果表明，生物人工瓣膜的状况主要影响频谱的较高部分（即高于250Hz），在故障情况下未发现频率成分。因此，将三个最高频率成分的幅度用作基于自适应单层感知器的分类器的输入向量，以识别正常和故障类别。对于所检查的样本集，该方法对正常和故障的Carpentier-Edwards（C-E）瓣膜给出了100%的正确判别。
