Abstract
Electrocardiogram (ECG) is a tool used for the electrical analysis of the status of human heart activity. When the ECG signal is recorded, it gets contaminated with different types of noises. So, for accurate analysis, noises must be eliminated from the ECG signal. There are different types of noises that contaminate the characteristics of ECG signal i.e Power line interference, baseline wander, Electromyogram (EMG). In this paper, different techniques have implemented for the removal of noises. A median filter is used for removal of DC component and Savitzky-Golay filter (SG) is used for smoothing noised waveform and then wavelet transform (db4) is used to decompose the ECG signal for removal of various artifacts. Wavelet transform provides the information in frequency and time domain and then thresholding has been applied for the implementation of algorithms in MATLAB. The measured results i.e. SNR(Signal to Noise ratio) and MSE(Mean square error) have been calculated using different databases like MIT-BIH, Long-term ST database, European ST-T database. The results are examined with proposed methods that are better than those reported in the literature.
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