Artifacts elimination in impedance cardiography signals using median adaptive algorithms

Abstract

In the recent years, elimination of the artifact from Impedance Cardiography (ICG) signals is an active area. For monitoring the cardiac output, stroke volume and other hemodynamic parameters are assessed by using ICG which is non-invasive method. While acquiring the ICG signal this method affected by various non-stationary artifacts such as respiration artifacts (RA), muscle artifacts (MA), electrode artifacts (EA) and sinusoidal artifacts (SA) leads to difficulty in diagnosis. Hence for accurate diagnosis we proposed several hybrid adaptive filtering techniques having hybrid variants like Median LMS (MLMS), Sign Regressor MLMS(SRMLMS), Sign MLMS (SMLMS), Sign Sign MLMS (SSMLMS). Based on these hybrid algorithms we developed the adaptive signal enhancement units (ASEUs) and evaluated the performance of ICG signal components obtained from MIT-BIT database. Among these algorithms ASEU performance by the SRMLMS gives the better filtering technique. The parameter of signal to noise ratio improvement (SNRI) for SA, RA, MA and EA are measured as 8.6926 dBs, 4.6278 dBs, 7.4453 dBs and 7.8061 dBs respectively. Hence for ICG signal filtering in real time health care sensing systems SRMLMS based ASEUs are more suitable for better performance.