ECG denoising using mutual information based classification of IMFs and interval thresholding

Abstract

International audienceThe 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

De-noising of Signal using FIR Filters for Hearing Aids

“…To improve the signal quality different approaches have been performed by many researchers. Our approach uses DWT [4] (Discrete wavelet Transform) method which uses a small wave, where energy is concentrated at a specific point and it transform means representing signals into a different form, then the signal is decomposed using EMD [5] method which helps in extracting functions called IMFs [6] (intrinsic mode functions) from the signal. A signal is uncorrelated into number of IMFs but these contain noise.…”