Adaptive Filtering and Characteristics Extraction for Impedance Cardiography

Impedance Cardiography (ICG) is a noninvasive technique for monitoring stroke volume, cardiac output and other hemodynamic parameters, which is based on sensing the change of thoracic electrical impedance caused by blood volume change in aorta during the cardiac cycle. Motion artifact and respiratory artifact can lead to baseline drift in ICG signal, particularly during or after exercise, which can cause errors when calculating hemodynamic parameters. This paper presents an LMS-based adaptive filtering algorithm to suppress the respiratory artifact of ICG signal without restricting patients’ breath. Estimation of hemodynamic parameters requires error-free automatic extraction of the characteristic points. Wavelet transform is used for extracting characteristic points which include its peak point (Z), start point (B) and end point (X) of left ventricular ejection time.