Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering

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.

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

“…The methods proposed in this paper can combine some statistical optimal strategies [44][45][46][47] to study the parameter estimation algorithms of linear and nonlinear systems [48][49][50][51][52] and can be applied to other fields, [53][54][55][56][57][58][59] such as fault detection, image processing, and sliding mode control. Different from the previous linearization method like Taylor expansion, we take use of the special structure of the bilinear system and propose the state filtering algorithm to obtain the unknown states by minimizing the covariance matrix of the state estimation errors based on the extremum principle.…”

On some parameter estimation algorithms for the nonlinear exponential autoregressive model

“…The methods proposed in this paper can combine some statistical methods 47 to study the parameter identification and state filter design for different systems with colored noise [48][49][50][51][52][53][54][55] and can be applied to other fields. [56][57][58][59]…”

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

“…the forgetting factor BSO-HMISG algorithm (BSO-FF-HMISG) algorithm is formulated to improve the convergence speed and the parameter estimation accuracy of the BSO-HMISG algorithm. The proposed state and parameter estimation algorithms for bilinear systems can combine other estimation algorithms 45,46 and the mathematical tools [47][48][49][50][51] and strategies [52][53][54][55][56] to explore new identification methods of other linear, bilinear, and nonlinear systems with colored noises [57][58][59][60][61] and can be applied to other fields such as information processing [62][63][64][65][66] and communication. [67][68][69][70][71] Remark 6.…”