Heart Rate Calculation by Detection of R Peak · Keywords- MATLAB stimulator, QRS complex, Hilbert transforms, R peak, wavelet transform I. INTRODUCTION The electrocardiogram (ECG)

DOI:10.23883/IJRTER.2018.4318.TTBCC 71

Heart Rate Calculation by Detection of R Peak

Aditi Sengupta Department of Electronics & Communication Engineering, Siliguri Institute of Technology

Abstract- Electrocardiogram (ECG) is one of the most common bioelectrical 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 in determining heart rate variability (HRV). This paper employs Hilbert transforms

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,

artefacts, 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.

Keywords- MATLAB stimulator, QRS complex, Hilbert transforms, R peak, wavelet transform

I. 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

Until now, various methods have been reported by researchers for detection of QRS complex such as

differentiation methods, digital filters, filter banks, genetic algorithm, and maximum a posterior

(MAP) estimator. used differentiator operator for detection of QRS complex; 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. In

fact, most of the presented methods have a fundamental problem known as sensitivity to noise.

Although, wavelet filters can be proposed for solving this problem, however, the problem of

sensitivity to noise does not solve in these systems completely. In this paper, we try to decrease the

sensitivity to noise by selecting an optimal combination among offered techniques.

In addition to the proposed methods, experimental techniques and averaging in signal decomposition

using partial derivatives and wavelet transforms and also methods based on neural networks have

been proposed for the detection of QRS complex and R wave. Methods based on experimental

techniques or differentiation usually have high sensitivity to noise and methods based on neural

networks are less used because of complication of their designing and learning.

II. DETECTION PROCESS

• An electrocardiogram (ECG or EKG) means recording of electrical activity of the heart.

Small electrical impulses are created in the heart by so-called pacemaker cells. These impulses

spread through the heart muscle and make it contract. ECG records these signals as they travel

through the heart. To the trained specialists, ECG provides large amount of information about the

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 04, Issue 06; June - 2018 [ISSN: 2455-1457]

@IJRTER-2018, All Rights Reserved 72

structure and the function of the heart. ECG is widely used to detect various abnormalities in heart

rhythm, size of the heart chambers or possible damage to the heart muscle or its nervous system.

• Heart rate variability (HRV) is calculated based on variation of time in milliseconds between

two heartbeats. HRV varies as you breathe in and out and is a relatively new method for assessing. If

we take stress as an example. What makes HRV interesting is the fact that it can reflect changes in

stress while other physiological parameters, like blood pressure, sugar level are still in normal or

accepted ranges. That is why HRV is becoming increasingly popular parameter in the fields of sports

and sports science, corporate health, cardiology, ergonomics, diabetes care and relaxation training

therapy. HRV is also being widely used on physiological research of autonomic nervous system.

• 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. 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.

III. PROCESSED ALGORITHM

1. Copy matlab files – Ecgdemo.m

a. Ecgdemowinmax.m

[1] Ecgdemodata1.mat

[2] Ecgdemodata2.mat

b. (all the files except this “readme.txt” file) into Matlab’s work directory

2. Start up Matlab

3. Type >>ecgdemo and press enter

4. Outputs are two figures which have 6 plots:

a. The first plot shows original ECG data.

b. The second plot contains ECG data correcting the low frequency.

c. The third plot shows the data after first filtering pass, the filter window is of default size so

the result is not “clear”.

d. The fourth plot shows detected peaks on the stage some peaks can be skipped

e. The result of peak detection and optimize the filter window size is analysed. So the fifth plot

contains the result of 2-d filtering pass;

f. Sixth plot shows the final result.



IV. FLOW CHART

No

Yes

Load ECG mat file

data in matlab

Initialize sampling rate

and sampling points

Selection of parameters

[M=windows

length(M<<N),D=R-wave

duration, Thresholds]

For i=1: N-M

High Order Statistics

Algorithm parameters of

Χ(1) I=i:i+M

Kurtosis and

Skewness parameters

for local maximum of

1st derivative

If threshold

is less

Threshold

Next i

R-wave

point=i+M

Thresholds

Update

i=i+D-1



V. RESULTS

IMAGES FOR DIFFERENT STEPS

Figure 1. Original ECG Data Figure 2. ECG Data correcting low frequency

Figure 3. R peaks by Wavelet Transform Figure 4. R peaks Expert Annotation

Figure 5. Comparison of Wavelet and QRS Complex Figure 6. Final Result

VI. CONCLUSION

• To analyse the detection of R-peak, filtered ECG signal passes through the moving window

integrator. The window width of the filter correlates to the heart rates, improving the detection

quality. However, the window output signal goes to the local peak detector to check whether the

samples differ from the previous value or not. The QRS-detector itself gets the filtered ECG signal

and binary output of the R peak detector. Therefore, .it is only detected, if a peak in the ECG signal

is detected.



• On the other hand, undetected R-peaks always result in the loss of information. If no R-peak

is detected for a certain time, then the High Order Statistics Algorithm is used, so then the peaks with

lower amplitude can also be detected due to this reason. Moreover, double heartbeats can be detected

too, if the amplitude of the R-wave is too high.

REFERENCES I. Benmalek M, Charef A. Digital fractional order operators for R-wave detection in electrocardiogram signal. IET

Signal Processing. 2009;3:381–91.

II. Xue Q, Hu YH, Tompkins WJ. Neural-network based adaptive matched filtering of QRS detection. IEEE Trans

Biomed Eng. 1992;39:317–29.

III. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32:230–6.

Documents

Heart Rate Calculation by Detection of R Peak · Keywords- MATLAB stimulator, QRS complex, Hilbert transforms, R peak, wavelet transform I. INTRODUCTION The electrocardiogram (ECG)