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ECG noise reduction using empirical mode decomposition based on combination of instantaneous half period and soft-thresholding
, Article Middle East Conference on Biomedical Engineering, MECBME ; 2014 , p. 244-248 ; Shamsollahi, M. B ; Sharif University of Technology
Abstract
The electrocardiogram (ECG) signal is widely used for diagnosis of various types of cardiac diseases. However, in practical cases, the signal is corrupted by artifacts through the recording process. Thus, denoising of this type of biological signals seems necessary. Several methods have been suggested in recent years for the purpose of ECG denoising; some of which have been based on Empirical Mode Decomposition (EMD). In this paper, an EMD-based approach is proposed which uses the time interval between two adjacent zero crossings within an Intrinsic Mode Function (IMF), defined as Instantaneous Half Period (IHP), to distinguish noise components from the main ECG signal. Noisy signal is...
ECG denoising using mutual information based classification of IMFs and interval thresholding
, Article 2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015, 9 July 2015 through 11 July 2015 ; July , 2015 , Page(s): 1 - 6 ; 9781479984985 (ISBN) ; Shamsollahi, M. B ; Senhadji, L ; Molnar K ; Herencsar N ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
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...