Gowri T.

Work place: Dept. of ECE, GIT, GITAM University, Visakhapatnam-530045, A.P, INDIA

E-mail: gowri3478@yahoo.com


Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Information Security, Information Systems


T. Gowri, She received B.Tech from Nagarjuna University, and M. Tech from Jawaharlal Nehru Technological University.

She is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering, GIT, GITAM University, Visakhapatnam, A.P, India. Her research interests include Digital Information Systems and Computer Electronics, Digital Signal Processing and Information Security. 2001. She is currently pursuing the Ph.D degree in the Department of Electronics and Communication Engineering from Jawaharlal Nehru Technological University Kakinada, India. She has more than 15 years experience of Teaching under graduate and post graduate level.

Author Articles
Muscle and Baseline Wander Artifact Reduction in ECG Signal Using Efficient RLS Based Adaptive Algorithm

By Gowri T. Rajesh kumar P.

DOI: https://doi.org/10.5815/ijisa.2016.05.06, Pub. Date: 8 May 2016

When we acquiring the Electrocardiogram (ECG) signal from the person, the signal amplitude (PQRST) and timing values are changes due to various artefacts. The different artefacts are Baseline wander, power line interference, muscle artefact, motion artefact and the channel noise also added sometimes during the transmission of the signal for diagnosis purpose. The adaptive filters play vital role for reduction of noise in the desired signals. In this paper we proposed, block based error normalized Recursive Least Square (RLS) adaptive algorithm and sign based RLS adaptive algorithm, which are used for reduction of muscle artifact noise and base line wander noise in the ECG signal. From the simulation result we analyzed that, comparing to Least Mean Square algorithm, the proposed RLS algorithm gives fast convergence rate with high signal to noise ratio and less mean square error.

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