ECG Denoising Using Mutual Information Based Classification of IMFs and Interval Thresholding

The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and power line interface. In this paper, a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed. We use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods we use the combination of mutual information and two EMD based interval thresholding approaches. Our new method is evaluated on ECG signals available in MIT-BIH database. This method is compared with two EEMD based interval thresholding methods. The results show that our proposed method has a better Signal to Noise Ratio improvement (SNRimp) and a lower Mean Square Error (MSE) than the other two methods.