Open AccessArtifact Elimination in Impedance Cardiography using Gradient based Adaptive Signal Enhancement Techniques
Author(s) -
Md. Zıa Ur Rahman,
ShafiShahsavar Mirza,
K. Murali Krishna
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1632.078219
Subject(s) - impedance cardiography , artifact (error) , signal (programming language) , adaptive filter , stroke volume , computer science , least mean squares filter , biomedical engineering , noise (video) , electrical impedance , medicine , artificial intelligence , image (mathematics) , heart rate , engineering , algorithm , blood pressure , electrical engineering , programming language
Impedance Cardiography (ICG) is a noninvasive method for indirect measurement of stroke volume, monitoring the cardiac output and observing the other hemodynamic parameters by the blood volume changes in the body. The blood volume changes inside a certain body segment due to a number of physiological processes are extracted in the form of the impedance variations of the body segment. The ICG analysis provides the heart stroke volume in sudden cardiac arrest. In the clinical environment desired ICG signals are influenced by several physiological and non-physiological artifacts.As these artifacts are not stationary in nature, we proposed adaptive filtering techniques to eliminate the artifacts. In this paper we used Least Mean Square (LMS), Least Mean Fourth (LMF), Median LMS (MLMS), Leaky LMS (LLMS), and Dead Zone (DZLMS) adaptive techniques to eliminate artifacts from the desired signals. Several adaptive signal enhancement units (ASEUs) are developed based on these adaptive techniques, and evaluated on the real ICG signal components. The ability of these algorithms is evaluated by performing the experiments to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the DZLMS based ASEU performs better in the filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 11.9140 dB, 7.3657 dB, 10.4060 dB and 10.5125 dB respectively for SA, RA, MA and EA. Hence, the DZLMS based ASEUs are well suitable for ICG filtering in the real time health care monitoring systems.