Artifacts Elimination in Impedance Cardiography Signals using Median Adaptive Algorithms
Md. Zia Ur Rahman1, ShafiShahsavar Mirza2, K. Murai Krishna3
1Md Zia Ur Rahman, Department of Electronics and Communication Engineering, K L University, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (Andhra Pradesh), India.
2ShafiShahsavar Mirza, Department of Electronics and Communication Engineering, Eswar College of Engineering, Kesanupalli, Narasaraopeta Guntur (Andhra Pradesh), India.
3K. Murali Krishna, Department of Electronics and Communication Engineering, KKR & KSR Institute of Technology & Sciences, Vinjanampadu, Guntur (Andhra Pradesh), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 895-898 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7375068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Adaptive Filter, Impedance Cardiography, Hemodynamic Parameters, Stroke Volume, Signal Enhancement
Scope of the Article: VLSI Algorithms