Abstract
The ECG signal (electrocardiogram) is the biomedical signal used in clinical studies for the diagnosis of cardiovascular diseases. ECG is the electrical representation of the cardiac activity and is obtained by placing the electrodes on the patient’s chest. In this process, several noises appear due to muscular contractions related to breathing and electronic interference. Thus, there is a need for removal of noise for better clinical evaluation. In this work, the empirical mode decomposition (EMD)-based method is proposed, that is, EMD threshold on long-term ECG signal. The purpose of this EMD-based technique is to decompose the signal into a few oscillatory parts, i.e., intrinsic mode functions (IMF’s). The IMF’s which are dominated by noise are immediately determined and removed by hard threshold method. For the evaluation of this technique, long-term (22,500 samples) ECG signals are acquired from open source MIT-BIH databases. Finally, the performance indicators such as mean square error (MSE) and signal to noise ratio (SNR) in dB of hard threshold method are calculated by using the MATLAB 2018a software.
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