Denoising of ECG Signals with Adaptive Filtering Algorithms & Patch Based Method

ECG signals have been proven a very versatile tool for detection of cardiovascular diseases. But during recording of these signals, the ECG data gets contaminated by various noise signals caused by power line interference, base line wander, electrode movement, muscle movement (EMG) etc. These noise signals are known as artifacts. These artifacts mislead the diagnosis of heart which is not desired. To avoid this problem caused by artifacts, removal of these artifacts has become essential. There are various techniques which have been used for artifacts rejection from ECG. Conventional filters remove the artifacts up to some extent but these filters are static filters. These filters cannot update their coefficients with change in environment. Hence adaptive filters, now days, are used for artifact removal from ECG signals. Adaptive filters update their coefficients according to the requirement. There are various adaptive algorithms such as Leas Mean Square (LMS), Recursive Least Square (RLS), Normalized Least Mean Square (NLMS) etc are present. Moreover, there is one more method is described which is patch based and used for artifact rejection from ECG signals. This method was previously used only for image denoising but now it has been using for artifact rejection from biomedical signals. In this paper, Least Mean Square (LMS) algorithm and patch based method has been implemented for denoising the ECG signal. KeywordsECG, EMG, LMS, NLMS, RLS