Denoising ECG Signal using Adaptive Filter Algorithms and Cubic Spline Interpolation for Regaining Missing data points of ECG in Telecardilogy System

Maintaining one’s health is a fundamental human right although one billion people do not have access to quality healthcare services. Telemedicine can help medical facilities reach their previously inaccessible target community. The Telecardiology system designed and implemented in this research work is based on the use of local market electronics. In this research work we tested three algorithms named as LMS (Least Mean Square), NLMS (Normalized Mean Square), and RLS (Recursive Least Square).We have used 250 mV amplitude ECG signal from MIT-BIH database and 5mV(2 % of original ECG signal), 10 mV(4% of original ECG) 15mV (6% of original ECG),20 mV(8% of original ECG signal) and 25mV(10% of original ECG signal) of random noise and white Gaussian noise is added with ECG signal and Adaptive filter with three different algorithms have been used to reduce the noise that is added during transmission through the telemedicine system. Normalized mean square error was calculated and our MATLAB simulation results suggest that RLS performs better than other two algorithms to reduce the noise from ECG. During analog transmission of ECG signal through existing Telecommunication network some data points may be lost and we have theoretically used Cubic Spline interpolation to regain missing data points. We have taken 5000 data points of ECG Signal from MIT-BIH database. The normalized mean square error was calculated for regaining missing data points of the ECG signal and it was very less in all the conditions. Cubic Spline Interpolation function on MATLAB platform could be a good solution for regaining missing data points of original ECG signal sent through our proposed Telecardiology system but practically it may not be efficient one