ECG signal de-noising using complementary ensemble empirical mode decomposition and Kalman smoother

The Electrocardiogram (ECG) is a representation of the electrical events of the cardiac cycle. Each event has a distinctive waveform and the study of wave form can lead to greater insight into a patient's cardiac pathophysiology. ECG is the biological signal and it represents the electrical activity of the heart. The signal is recorded by placing the electrode on the human body surface and the recorded signal includes several types of noises such as power line interference, electrode contact noise, muscle contractions, baseline wander, electro surgical noise, instrumental noise muscle contractions and composite noise. The proposed work is to develop a system which is used for removing or filtering the artifacts present in the given input signal. The input signal is an synthetic signal which consists of power line interference noise and composite noise as artifacts. The Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Kalman Smoother methods are used for developing de-noising system for effective filtering of noise which is generated during the ECG signal recording. The combination of two methods are proposed in this work for better filtering performance. Pre-processing of the given input signal is performed by using band pass filter and decomposition of signal by wavelet transformation methods. The developed system performance can be evaluated by using SNR (Signal to Noise Ratio) and RMSE (Root Mean Square Error) and the results are tabulated. The results shows better performance and strongly recommend that, combined system performance gives better result compare to individual system results.