【摘要】 本文以心音特征为基础,实现了连续的左心室收缩压预测。通过对3只比格犬进行实验,以肾上腺素诱发心脏血流动力学发生变化,然后同步采集实验犬的心音、心电、左心室血压等信号,共获取了28组有效数据。通过提取心音特征,借助人工神经网络实现了反推左心室收缩血压,获得了较好的预测效果。本研究在较大的血压动态变化范围内,得到了绝对误差均值仅为7.3 mm Hg、预测血压与测量血压的平均相关系数为0.92的实验结果。研究结果显示,本文所述方法有助于实现无创的左心室血流动力的连续监测。更多还原

【Abstract】 The continuous left ventricle blood pressure prediction based on selected heart sound features was realized in this study. The experiments were carried out on three beagle dogs and the variations of cardiac hemodynamics were induced by various dose of epinephrine. The phonocardiogram, electrocardiogram and blood pressures in left ventricle were synchronously acquired. We obtained 28 valid recordings in this study. An artificial neural network was trained with the selected feature to predict left ventricular blood pressure and this trained network made a good performance. The results showed that the absolute average error was 7.3 mm Hg even though the blood pressures had a large range of fluctuation. The average correlation coefficient between the predicted and the measured blood pressure was0.92. These results showed that the method in this paper was helpful to monitor left ventricular hemodynamics noninvasively and continuously.更多还原