Research on electrocardiogram baseline wandering correction based on wavelet transform, QRS barycenter fitting, and regional method

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

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.

RELATIONSHIP AMONG QRS COMPLEX CHARACTERS IN ELECTROCARDIOGRAM AND ITS APPLICATION TO MYOCARDIAL ISCHEMIA

Electrocardiogram (ECG) is a noninvasive, economic, and convenient detecting tool in myocardial ischemia (MI), and its clinical appearance is mainly exhibited by ST-T complex changes. Recently, QRS complex characters in detecting MI were proposed by an increasing number of researchers. In this paper, various QRS complex characters were extracted in ECG, and their relationship was analyzed systematically. As a result, these characters were divided into two groups, and there was good correlation among them in each group, while the correlation between the groups was poor. Finally, these QRS complex characters were applied to myocardial ischemia, and five characters had significant differences after 59 normal ECG recordings verification, which were: QRS upward and downward slopes, transient heart rate, angle R and angle Q in a triangle QRS. Experimental results showed it was apparent that the trend changes of these five characters when MI events occurred were consistent with their relationship. The conduction velocity of action potentials in ventricular depolarization is slower in MI states than in normal states.

A new approach of QRS complex detection based on matched filtering and triangle character analysis

QRS complex detection usually provides the fundamentals to automated electrocardiogram (ECG) analysis. In this paper, a new approach of QRS complex detection without the stage of noise suppression was developed and evaluated, which was based on the combination of two techniques: matched filtering and triangle character analysis. Firstly, a template of QRS complex was selected automatically by the triangle character in ECG, and then it was time-reversed after removing its direct current component. Secondly, matched filtering was implemented at low computational cost by finite impulse response, which further enhanced QRS complex and attenuated non-QRS regions containing P-wave, T-wave and various noise components. Subsequently, triangle structure-based threshold decision was processed to detect QRS complexes. And RR intervals and triangle structures were further analyzed for the reduction of false-positive and false-negative detections. Finally, the performance of the proposed algorithm was tested on all 48 records of the MIT-BIH Arrhythmia Database. The results demonstrated that the detection rate reached 99.62 %, the sensitivity got 99.78 %, and the positive prediction was 99.85 %. In addition, the proposed method was able to identify QRS complexes reliably even under the condition of poor signal quality.