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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.

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The dynamic threshold algorithm (DTA) presented by Pan Tompkins is a popular QRS detection method, and it has high sensitivity and specificity. However, the accuracy of this algorithm would be compromised if its sensitivity is increased. In this study, an enhanced dynamic threshold algorithm (EDTA) based on dynamic threshold rules is proposed, which add a compensation scheme to reduce the rate of misdetection and missed detection of R wave in low signal-to-noise ratio condition, sensitivity and detection error rate are calculated on simulated and clinical data to compare the performance between EDTA and DTA, and EDTA yields a competitive results. For the clinical data, the average accuracy rate of EDTA is 99.24%, which is higher than that of DTA at 95.98%. Further compared experiments among EDTA and the two other popular algorithms are conducted and the results of their validation over a public database are given and discussed, which prove our superiority.