Denoising Ecg Signals Using Adaptive Filter Algorithm
Chinmay Chandrakar1, M.K. Kowar2
1Chinmay Chandrakar, Electronics & Telecommunication, Shri Shankaracharya College of Engineering & Technology, Bhilai, India.
2Dr. M. K. Kowar, Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India.
Manuscript received on January 01, 2012. | Revised Manuscript received on February 04, 2012. | Manuscript published on March 05, 2012. | PP: 120-123 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0396012112/2012©BEIESP
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© The Authors. Published By: 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: One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields, random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper type of adaptive filters are considered to reduce the ECG signal noises like PLI and Base Line Interference. Results of simulations in MATLAB are presented. In this we have used Recursive Least Squares (RLS). RLS algorithm is proposed for removing artifacts preserving the low frequency components and tiny features of the ECG. Least-squares algorithms aim at the minimization of the sum of the squares of the difference between the desired signal and the model filter output .When new samples of the incoming signals are received at every iteration, the solution for the least-squares problem can be computed in recursive form resulting in the recursive least-squares (RLS) algorithms. The RLS algorithms are known to pursue fast convergence even when the Eigen value spread of the input signal correlation matrix is large. These algorithms have excellent performance when working in time-varying environments. All these advantages come with the cost of an increased computational complexity and some stability problems, which are not as critical in LMS-based algorithms.
Keywords: ECG Signal, Dirichlet’s Condition, Adaptive Filter