Biomedical Engineering – From Theory to Applications

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76 Biomedical Engineering – From Theory to Applications the baseline wander corrections, filtering method is most widely used. Here, we will describe the linear filtering method to remove the baseline wander in ECG signals. The frequency of the baseline wander is usually below 0.5 Hz (Laguna et al. 2005). This information particularly helps in design a high-pass filter in order to get rid of baseline wander. The design of a linear time-invariant high pass filter requires several considerations, most importantly, the choice of cut-off frequency and filter order. The ECG characteristic wave frequencies are higher than baseline wander. Therefore, a carefully designed high pass filter with cut-off frequency, Hz can effective remove the baseline. Baseline wander correction using a linear digital filter is shown in Figure 11. Instead of a high-pass filter we used band-pass filtering. We applied a sixth order butterworth filter with cutoff 0.7-40 Hz. To avoid distortion, zero phase digital filtering was performed by processing the data in both forward and reverse direction. This can be done using MATLAB command filtfilt. Fig. 11. ECG signal after band-pass filtering which effectively corrects baseline wander and reduces low frequency noise and high frequency artifacts. Prior to applying bandpass filter the DC components were removed from ECGs. 4.2 QRS detection Detection of QRS complex is particularly most important in ECG signal processing. The information obtained from QRS detection, temporal information of each beat and QRS morphology information can be used for the improvement of performance for the other algorithms. In literature, many methods have been developed for detection of QRS complex. Most of these are based on filtering, nonlinear transformation and decision rules as well as using template matching method. In recent approaches, wavelet transform and empirical mode decomposition based algorithms have been developed. We will discuss a widely used
Biomedical Signal Transceivers and robust real-time QRS detection algorithm popularly known as Pam-Tompkins algorithm (Pan & Tompkins, 1985). The Pan-Tompkins algorithm consists of several steps. This is a single channel detection method in order to achieve better performance. In order to attenuate noise, the signal is first passed through a digital band-pass filter composed of cascaded high-pass and low-pass filters (Pan & Tompkins, 1985). The band-pass filtering corrects the baseline wander, reduces muscle noise, 60 Hz power line interference, T-wave interference (Pan & Tompkins, 1985). The cutoff frequency of this band-pass filter is an important design parameter. Another reason of this filtering is to maximize the energy of QRS complex. Pan and Tompkins suggested choosing 5-15 Hz as desirable pass band. The next steps involve nonlinear transformation in order to highlight the QRS complex from baseline. These steps are differentiation, squaring followed by moving window integration. Derivative stage provides the slope information of QRS complex. Squaring operation intensifies the slope of the frequency response curve. It also helps to restrict the false positives caused by unusually high amplitude T waves. The signal produced by the moving window integration operation provides the slope and width of the QRS complex. The choice of window sample size is an important parameter. Generally, it should be chosen such a way that the window size is same as the QRS width. The window width can be determined empirically making sure that QRS complex and T waves do not merge together. For practical application, we choose window of 30 samples (150 ms, sampling frequency was 360 samples/s). Finally, an adaptive threshold will be applied to identify the location of QRS complexes (Pan & Tompkins, 1985). 500 0 -500 0 500 1000 1500 100 0 -100 0 500 1000 1500 4000 2000 0 0 500 1000 1500 x 104 4 2 (d) 0 0 500 1000 1500 500 0 0 500 1000 1500 Samples Fig. 12. Typical steps of Pan-Tompkins algorithm for detecting QRS complex: (a) band-pass filtered ECG signals; (b) after differentiation; (c) after performing squaring operation; (d) moving window integration; and (d) R peak detection. (a) (b) (c) (e) 77
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BIOMEDICAL ENGINEERING - FROM THEOR
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180 16. Acknowledgments Biomedical
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190 R* M M M M MR*M O O O O M O R*
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