Zhang Jinghui, Tang Hong
Department of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China.
Department of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024,
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2017 Jun 1;34(3):335-341. doi: 10.7507/1001-5515.201606068.
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 was 0.92. These results showed that the method in this paper was helpful to monitor left ventricular hemodynamics non-invasively and continuously.
本研究实现了基于所选心音特征的连续左心室血压预测。实验在三只比格犬身上进行,通过不同剂量的肾上腺素诱导心脏血流动力学变化。同步采集心音图、心电图和左心室血压。本研究共获得28个有效记录。使用所选特征训练人工神经网络以预测左心室血压,该训练后的网络表现良好。结果表明,尽管血压波动范围很大,但绝对平均误差为7.3毫米汞柱。预测血压与实测血压之间的平均相关系数为0.92。这些结果表明,本文方法有助于无创且连续地监测左心室血流动力学。