Abstract
One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.
Highlights
Electrocardiogram (ECG) is an important clinical tool for heart disease diagnosis; precision of ECG is a matter of life and death
The quality of the ECG signal is degraded during acquisition due to the interferences including power line harmonics, motion artifact, and baseline wander (BW), which makes it difficult to identify the factors which reflect the characteristics of physiological activity
Is study introduces a combined algorithm (CA) of mathematical morphological filters (MMF)- and WT-based filtering for BW suppression. e CA can effectively preserve the outline of the BW and avoid waveform distortions caused by morphology filters, thereby obtaining an enhanced ECG quality
Summary
Electrocardiogram (ECG) is an important clinical tool for heart disease diagnosis; precision of ECG is a matter of life and death. The high-pass filter is capable of suppressing BW, the ECG waveform distortion is inevitable because of the frequency variations of the ECG signal. A number of advanced BW suppression algorithms including linear low-pass filters, nonlinear filters, polynomial interpolation, wavelet filters, and mathematical morphological filters (MMF) are proposed [3,4,5,6,7,8,9]. WT method exhibits relatively good effects for BW suppression This method causes T waveform distortions due to the frequency overlaps between the high-scale approximate coefficients and T wave. E CA can effectively preserve the outline of the BW and avoid waveform distortions caused by morphology filters, thereby obtaining an enhanced ECG quality.
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