Calculation of cardiac hemodynamic parameters is based on accurate detection of feature points in impedance cardiogram. According to these parameters, doctors can determine heart conditions, so it is very important to accurately detect the feature point of impedance differential signals. This article presents a process in which we used wavelet threshold method to de-noise signals, and then detected the feature points after six layers wavelet decomposition by using bior3.7. The experimental data were collected from healthy persons in our laboratory and twenty two clinical patients in Chongqing Daping Hospital by using KF_ICG instrument. The results indicated that this method could precisely detect feature points whether it was from healthy people or clinical patients. This helps to achieve the application of noninvasive detection cardiac hemodynamic parameters in clinical treatments by using impedance method.
The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.