25-02-2013, 12:15 PM
R Peak Detection in Electrocardiogram Signal Based on an Optimal Combination of Wavelet Transform, Hilbert Transform, and Adaptive Thresholding
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A B S T R A C T
Electrocardiogram (ECG) is one of the most common biological signals which play a significant role in the diagnosis of heart diseases.
One of the most important parts of ECG signal processing is interpretation of QRS complex and obtaining its characteristics. R wave
is one of the most important sections of this complex, which has an essential role in diagnosis of heart rhythm irregularities and also in
determining heart rate variability (HRV). This paper employs Hilbert and wavelet transforms as well as adaptive thresholding method
to investigate an optimal combination of these signal processing techniques for the detection of R peak. In the experimental sections
of this paper, the proposed algorithms are evaluated using both ECG signals from MIT-BIH database and synthetic data simulated in
MATLAB environment with different arrhythmias, artifacts, and noise levels. Finally, by using wavelet and Hilbert transforms as well as
by employing adaptive thresholding technique, an optimal combinational method for R peak detection namely WHAT is obtained that
outperforms other techniques quantitatively and qualitatively.
INTRODUCTION
The electrocardiogram (ECG) signal is one of the most
important and well known biological signals used for
diagnosing people’s health. Detection of QRS complex is
one of the most important parts carried out in the ECG
signal analysis. QRS detection, especially detection of R
wave in heart signal, is easier than other portions of ECG
signal due to its structural form and high amplitude.
Till now, various methods have been reported by researchers
for detection of QRS complex[1-4] such as differentiation
methods,[5] digital filters,[6-10] neural networks,[11-13] filter
banks,[14,15] hidden Markov models,[16,17] genetic algorithm,[18]
and maximum a posterior (MAP) estimator.[19,20] Balda[21,22]
used differentiator operator for detection of QRS complex;
later this method was developed by Ahlstrom and
Tompkins.[23] Friesen[24] and Tompkins[25] also investigated
similar methods based on the sensitivity of QRS complex
to noise. Using ordinary filters is another class of methods
used for this purpose, but its high sensitivity to noise and
its incompatibility with frequency of input disorders cause
errors in the output of relative function. [
Proposed Method
In this section, several methods obtained from various
combinations of Hilbert transform, wavelet transform, and
adaptive thresholding for R wave detection are presented. For
better evaluation of the introduced algorithms, they are tested
on three ECG signals selected from MIT-BIH database (which have
three different noise levels) and results relative to each algorithm
have been shown separately. Figure 1 shows general plot of
three selected signals from mentioned database. It should be
noted that in all sections of the paper (except in signal modeling
section), these three signals are used as reference signals.
Preprocessing: QRS Complex Detection by using
Differentiator Operator
In this section, we are going to make transiting points of
zero more evident by using differentiation technique, which
has been used as pre-processing part in the next sections.
The reason is distinguishing QRS complex pattern in order to
simplify next stages of processing. Functions employed in the
first and second order derivatives method in this section are
as follows.[21] In this stage, place of QRS complex is identified
by using the first and second order derivatives and then
windowing technique have been used in order to smooth
signal.
Adaptive Thresholding Technique
Adaptive threshold technique is one of the significant
parts carried out for the detection of R wave peak. Various
methods have been used for this purpose including works
by Shubsa[38] and Li.[39] Using experimental algorithms, it is
observed that defining high values for threshold leads to
lack of proper detection and defining low values causes
incorrect detection of the peaks. In adaptive threshold
structure, detection is done by using a pair of threshold
limits named Up Limited Threshold (ULT) (eq. 4) and Down
Limited Threshold (DLT) (eq. 5). The proposed algorithm
works as follows:[35] If in each stage of threshold, the number
of detected peak by up and down limits is not equal, then
error component is defined and subtracted from the limits.
The mentioned threshold calculation procedure continues
till the two limits become equal and at the end the final
threshold limit is obtained.
CONCLUSION
The purpose of this paper was finding an optimal
combination of several introduced algorithms for R peak
detection in order to achieve better results, especially in
noisy environments. In this paper, we tried to introduce a
combinational method to decrease the sensitivity of R peak
detection procedure to noise. According to our results,
combination of wavelet transform, Hilbert transform,
and adaptive thresholding has a significant effect in the
detection of R wave and outperforms the others.