Artifact Elimination in Impedance Cardiography using Gradient based Adaptive Signal Enhancement Techniques
Md. Zia Ur Rahman1, Shafi Shahsavar Mirza2, K. Murai Krishna3 

1Md Zia Ur Rahman, Department of Electronics and Communication Engineering, K L University, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522502, Guntur, Andhra Pradesh, India.
2ShafiShahsavar Mirza, Department of Electronics and Communication Engineering, Eswar College of Engineering, Kesanupalli, Narasaraopeta-522601, Guntur, Andhra Pradesh, India.
3K. Murali Krishna, Department of Electronics and Communication Engineering, KKR & KSR Institute of Technology & Sciences, Vinjanampadu-522017, Guntur, Andhra Pradesh, India.

Manuscript received on 04 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 598-606 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1632078219/19©BEIESP | DOI: 10.35940/ijrte.B1632.078219
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Abstract: 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.
Index Terms: Adaptive Filter, Artifacts, Impedance Cardiography, Signal Enhancement, Stroke Volume.

Scope of the Article: Adaptive Systems