Analysis of hrv to study the effects of tobacco on ans among young indians

INTERNATIONAL JOURNAL OF ELECTRONICS AND
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 10, October (2014), pp. 65-73 © IAEME
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 5, Issue 10, October (2014), pp. 65-73
© IAEME: http://www.iaeme.com/IJECET.asp
Journal Impact Factor (2014): 7.2836 (Calculated by GISI)
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ANALYSIS OF HRV TO STUDY THE EFFECTS OF
TOBACCO ON ANS AMONG YOUNG INDIANS
D Mahesh Kumar1, Dr. Prasanna Kumar S.C2, Dr. B.G Sudarshan3, Yadhuraj S.R4
1Assistant Professor, Dept of Electronics and Instrumentation Engineering JSSATE, PhD Scholar at
Jain University Bangalore, India
2Professor & HOD, Dept of Electronics and Instrumentation Engineering, RVCE, Bangalore
3Assosiate Professor, Dept of Electronics and Instrumentation Engineering, RVCE, Bangalore
4Ph.D Scholar, Dept of Electronics and Instrumentation Engineering, RVCE, Bangalore
65
ABSTRACT
Smoking is one of the main causes of preventable death around the world. Statistics says that
at least half of the lifelong smokers dies before due to the ill effects caused by smoking. In this work
the ill effects caused because by tobacco smoking on the ANS (Autonomic Nervous System) is
analyzed using HRV (Heart Rate Variability). HRV is a time measure of difference between two
heart beats in sequence. The change in the heart rate or HRV occurs mainly because of the various
external and internal stimulation causes. HRV can be used to asses the cardiac and ANS functions.
The nonlinear methods of HRV analysis is speculated as one of the leading parameters for getting
the state of wellness of cardiac system and ANS system. In this study, the ECG data is collected
from different subjects of age group between 17 and 23. The HRV parameters have been extracted
from the collected data using Kubios software. The linear and nonlinear HRV analysis is carried and
it has been clearly reflects the completely different heart condition for the two data sets under study
among smokers and nonsmokers subjects by HRV measures. The heart rate among smokers is higher
when compared with nonsmokers. The LF/HF values denotes the ANS functions. The LF/HF values
are around 1.2 in nonsmokers and it is around 2.5 among smokers. The increase in LF/HF values
denotes the end increase in the activities of sympathetic nervous system and decrease in the activity
of parasympathetic nervous system among smokers. The TINN values are around 130 among
nonsmoking subjects and around 50 among smoking subjects. The nonlinear method like poincare
plot will gives the advantage of visually analyse the parameters. The points poincare plot in smokers
is much accumulated at the centre of the ellipse and in nonsmokers it is accumulated near the
periphery. The overall health of the cardiac function can be obtained by using the linear and
nonlinear HRV analysis.
Keywords: ECG, HRV, Smokers, Nonsmokers, ANS, Kubios Software.
IJECET
© I A E M E

66
1. INTRODUCTION
It changes according to the need obtained through evolution. Heart beat is not a linear process
it keeps varying to the conditions of the mental state and the external environment. Therefore HRV
continuously oscillates around the mean value of the RR interval. The entire human body functions
is controlled by Autonomic Nervous System (ANS). ANS also plays important role in homeostasis.
ANS mainly includes Sympathetic and Parasympathetic Nervous System. The cervical ganglia of the
ANS connects the sympathetic system. The T1 thoracic ganglia connects the parasympathetic system
which also influences on sweat glands in head and blood vessels. Sympathetic division increases the
force of action on the heart and hence the heart rate, Parasympathetic works as a complementary to
the sympathetic division and it tries to decrease the heart rate. The heart has evolved through ages to
function according to the need. The sympathetic system mainly works when there is a situation
demanding the tougher periods. On the other hand parasympathetic system will be more active
during resting period and provides the necessary conditions for the body to rest and helps in
rejuvenation. HRV is a tool used to obtain the variability of the heart rate invasively. HRV indirectly
provides the information relating to the sympathetic and parasympathetic activities and thus the
function of ANS. There are mainly three types of analysis done to obtain the parameters of HRV [2].
They are
1.1 Statistical Analysis: The statistical analysis are the common parameters which are obtained
from the series of successive RR interval values. It is also called time domain analysis. The
influential measure is the mean value of the RR interval. The alternate values measured in statistical
analysis is the NN50 denoting the intervals differing 50ms more than its previous intervals. Apart
from these the geometric parameters are obtained through histogram of RR interval. The most
important of such measure is the mean of RR interval values. The other measure obtained from
successive RR interval is NN50, which indicates the number of consecutive intervals differing 50ms
more than the previous intervals. Apart from the above measures, some geometric measures are
calculated from the histogram of RR interval. The HRV triangular index can be defined as the
integral of the histogram to the height of the histogram [11]. Another measure is the TINN[9], it is
the baseline width obtained from triangular interpolation.
1.2 Frequency Domain Analysis: In this method, from the RR interval series, we obtain the power
spectrum density (PSD). Generally in HRV analysis the frequency domain parameters are carried
either by FFT methods or AR modeling method [4]. The FFT based PSD is simple to implement,
while AR spectrum are used for short a samples to provide good resolution. Another advantage of
AR spectrum is it can be written as separate spectral components.
1.3 Nonlinear Methods: The nonlinear parameters are analyzed using many methods. Among them
Sample entropy [3], Recurrence plot analysis [2] and Correlation dimension [5] are the important
nonlinear methods. Sample entropy is a measure of complexity or irregularity in a signal. Recurrence
plot analysis is another approach for nonlinear analysis where recurrence plot is a symmetrical
matrix of zeros and ones. The structure of Recurrence plot matrix denotes the short line segments
which all are parallel to the main diagonal [1]. Another nonlinear method analysis of HRV is
correlation dimension, which is commonly used in measuring the complexity of the time domain
variables. It gives the information of the minimum number of dynamic variables that are required to
model the given system. Among nonlinear Poincare plot provides the reliable and visual data for the
analysis.

67
1.3.1 Poincare plot
One of the commonly used nonlinear method to obtain the nonlinear parameters of the data is
Poincare plot which is simple to interpret. The successive RR intervals correlation can be graphically
represented by Poincare plot i.e. plot of RRj+1 as a function of RRj. The shape of the plot denotes
the features of the data. There are many approaches to fit the plot, Kubios software designed to fit an
ellipse to plot in a parameterized shape. The ellipse is oriented according to the line-of-identity
(RRj = RRj+1) [7]. The short-term variability is SD1, standard deviation of the points which are
perpendicular to the line-of-identity. By obtaining SDSD time-domain measure SD1 can be written
as [12]
SD12=SDSD2 (7)
SD2 is the long-term variability which is the standard deviation with the line-of-identity. By
obtaining SDSD, SDNN time-domain measure SD2 can be written as
SD22=2SDNN2-SDSD2 (8)
Generally the Poincare plot of the first order is preferred because of its simplicity. As the
order increases the dimensions of the plot also increases.
2. MATERIAL AND METHODS
We studied a group of 144 subjects of age grouped between 17-23 years. The main objective:
to evaluate the prevalence of the autonomic cardiovascular complications in smoking personals.
Description of the groups: questionnaires were administered for general patient information (age,
sex, duration of disease, family history, and personal history), risk factors (alcoholism, sedentary
behaviour), treatment, and patient’s symptoms. The heart rate variability analysis was performed
with the help of the three lead ECG electrodes. The signals from the electrodes are amplified using a
bio amplifier which comes along with Skript Electronics modules. The output of the bio amplifier is
connected to the multichannel 6009 DAQ card, to acquire raw ECG signals from the output terminal
of ECG recorders. The sampling rate is typically set to 256 Hz. The acquired ECG signals were
stored in NI TDMS file type for further offline analysis. DAQ is connected to the computer using an
USB port for further processing of the signal. DAQ has to be configured inside Lab VIEW
environment. The obtained TDMS file is converted to the .txt file and the obtained ECG signal is
further filtered using MATLAB. The baseline trends and power interferences noises are removed in
MATLAB environment. The filtered ECG signal is fed to Kubios software to obtain the linear and
nonlinear parameters of HRV.
3. RESULTS
The electrical activity of the heart is obtained through ECG. ECG signals are acquired mainly
through two types namely invasive methods and noninvasive methods. Invasive methods are
accurate but it is not pragmatic. Non invasive methods are popular and commonly used while
measuring ECG. In this work we have used three lead surface electrodes. The signal obtained will be
corrupted by various noises like baseline shifts, power line interference and motion artifacts. Hence
processing of the signal is very important before we use them for analysis.

Figure 1: ECG signals corrupted with trend
There is a baseline drift in the above signal and hence its amplitude cannot be considered as
the original amplitude. Hence we must detrend the above signal before its amplitude is used for
measurements. The order polynomial is used to detrend the above signal.
Figure 2: Detrended ECG Signal
After removal of baseline drift, the motion artifact is removed by using simple 200 order
moving average filter in MATLAB. The powerline interference is removed by using notch filter.
The important part of the ECG signal is the QRS complexes which denotes the ventricular
depolarization of the heart. The peaks of the signal is detected in MATLAB which can be used for
feeding the signals to Kubios software.
Figure 3: ECG Signal
68

Figure 4: Peak Detected ECG Signal
69
HRV analysis using Kubios Tool
Kubios is an advanced software tool for studying the HRV. In this work Kubios tool is used
to study the variability of the RR intervals. The time interval plot between the each RR intervals,
which was obtained using MATLAB, is loaded to the Kubios tool.
Time-domain analysis
It is also called the statistical analysis and are common approach for studying HRV because
of its simplicity in application. The results of time-domain includes the mean RR intervals, Standard
deviation of RR interval, NN50, PNN50, TINN values and Triangular index values. Figure 5 shows
the results of time-domain analysis of the HRV signal.
Figure 5: Results of time-domain analysis of ECG signal.
Frequency-domain Analysis
In the frequency-domain methods, Discrete Fourier Transforms are applied to the RR interval
values in time domain to obtain a power spectrum density (PSD) estimate. The results of frequency-domain
are displayed by selecting the frequency-domain button on the top of the results view
segment. The frequency-domain results of HRV are shown in Figure 6.
Figure 6: Results of frequency-domain analysis of ECG signal.

70
Nonlinear Analysis
The nonlinear analysis has been analyzed using measures such as Poincare plot, detrended
fluctuation analysis, approximate entropy and recurrence plots. The results of non-linear are
displayed by pressing the nonlinear button on the top of the results view segment. The nonlinear
results of HRV are shown in Figure 7.
Figure 7: Results of nonlinear analysis of ECG signal
Our investigation demonstrated that overall HRV is markedly depressed in smoking subjects.
Smoking subjects had lower values for time-domain and frequency domain parameters than controls
(Table 1).
Table 1: Heart rate variability parameters in alcoholics group and in control group
Parameter Control group Alcoholics group
HR
(mean ± SD)
64±12 89±11
SDNN (ms)
(mean ± SD)
50±28 13±9
RMSSD
(mean ± SD)
55±20 15±8
LF (ms2)
(mean ± SD)
870±290 430±262
HF (ms2)
(mean ± SD)
740±123 170±117
LF/HF
(mean ± SD)
1.2±0.62 2.5±0.97
HRV parameters were significantly lower in the smoking subjects. From Table II, we can see
that the triangular index values of nonsmoking subjects are around 5.750 and for smoking subjects it
is around 3.077. Power law correlation in signal fluctuation and opposite heart condition of the two
types of subjects under study, Nonsmoking and smoking subjects, is reflected clearly from the RR
Triangular index value. So, by RR Triangular index we can easily differentiate between Nonsmoking
and smoking personnel.

Table II: RR Triangular Index: Values for 2 data sets (Nonsmoking and Smoking subjects)
Table III: TINN values for 2 data sets (Nonsmoking and smoking subjects)
From Table III, it is seen that Nonsmoking subjects have higher TINN value and smoking
subjects have lower TINN, thus clearly distinguishing the two groups. Disorder in the Heart rate
signal and opposite heart condition of the two types of subjects under study, Nonsmoking and
smoking, is reflected clearly from TINN value. So, by TINN Method, we can easily differentiate
between Nonsmoking and smoking subjects.
Table IV: Poincare Plot Analysis for 2 data sets (Nonsmoking and smoking subjects)
71
Nonsmoking Subjects
(Scaling exponent )
Smoking Subjects
(Scaling exponent )
5.750 3.077
4.864 2.851
5.368 3.157
4.961 3.286
5.146 2.792
5.239 2.884
5.658 2.957
4.689 3.059
4.781 2.746
Nonsmoking Subjects
(Value of TINN)
Smoking Subjects
(Value of TINN)
130 50
128 63
144 57
132 48
162 55
138 43
147 61
141 52
153 46
Nonsmoking Subjects
(Value of SD1)
Nonsmoking Subjects
(Value of SD2)
smoking Subjects
(Value of SD1)
smoking Subjects
(Value of SD2)
7.2 17.3 40.2 57.2
9.6 18.8 47.4 64.7
16.9 22.6 53.6 72.6
7.1 25.1 51.7 62.3
18.7 21.7 49.5 67.9
17.9 28.4 44.1 59.5
14.3 16.9 48.3 58.3
11.2 24.3 55.6 63.7
13.3 19.9 53.8 71.7

International Journal of Electronics and Communication Engineering Technology (IJECET), ISSN 0976 –
From table IV it is seen that the values of SD1 and SD2 are lesser in Nonsmoking subjects
comparatively to the smoking subjects. The Poincare plot can also be visually analysed and the
points under the ellipse shows the significant changes between the Nonsmoking and smoking
subjects.
4. CONCLUSION AND FUTURE PERSPECTIVES
The changes in the parameter values of the HRV analysis reflects the significant increment in
the sympathetic tone and possibly decrements in the parasympathetic system’s activity. Since,
cardiovascular function is a nonlinear process, the nonlinear methods like Poincare Plot,
Approximate Entropy Method and Detrended Fluctuation Analysis provides a more powerful
prognostic data than the statistically analysed HRV data. In this work we have compared the HRV
parameters between Smoking and Nonsmoking subjects. After analysing it is found that the two data
showed the different values indicating the different conditions of the cardiovascular regulation and
the ANS functions. The nonlinear parameters were found more reliable and it can be further used as
to anlayse the malfunctions in the regulation systems caused because of smoking. The RR triangular
index values are around 5.750 in nonsmoking subjects and around 3.077 in smoking subjects. Thus
we can distinguish between smoking and nonsmoking personals using RR triangular index values.
Another reliable nonlinear parameter which can be used is Poincare plot. The value of SD1 in
nonsmoking subjects are around 7.2 and in smoking personals it is around 40.2. The value of SD2 in
nonsmoking subjects are around 17.3 and in smoking subjects it is around 57.2. Hence the SD1 and
SD2 values of poincare plot are lower in nonsmoking subjects compared to the smoking subjects.
Thus HRV can forecast the illness that can be caused because of smoking. Since it is noninvasive
and can be easily used, to educate the smoking subjects to understand the ill effects caused by
smoking. The work should be carried on for different demographic data with different sex, age
groups and subjects with smoking related diseases.
72
REFERENCES
[1] M. Kobayashi, T. Musha, “1/f fluctuation of heart beat period,” IEEE Trans. Biomed. Eng.,
vol. 29, pp. 456-457, Jun. 1982.
[2] C. K. Peng, S. Havlin, J. M. Hausdorf, J. E. Mietus, H. E. Stanley, A. L. Goldberger, “Fractal
Mechanisms and Heart Rate Dynamics: Long-range Correlations and their Breakdown with
Disease,” Journal of Electro cardiology, vol. 28, supp. 1, pp. 59-65, 1995.
[3] S. M. Pincus, “Approximate a entropy as a measure of system complexity,” Proc. Nat. Acad.
Sci. USA, vol. 88, pp. 2297-2301, Mar. 1991.
[4] Serhat Balcioglu, MD, Ugur Arslan, MD, Sedat Türkoglu, MD, Murat Özdemir, MD, and
Atiye Çengel, MD - Heart Rate Variability and Heart Rate Turbulence in Patients with Type
2 Diabetes Mellitus With Versus Without a Cardiac Autonomic Neuropathy - Am J Cardiol
2007;100:890–893
[5] Task Force of the European Society of Cardiology and the North American Society of Pacing
and Electrophysiology. Heart rate variability guidelines. Standards of a measurement,
physiological interpretation, and clinical use Eur Heart J 1996; 17, 354 – 381.
[6] Moody GB, Mark RG, Goldberger AL, PhysioNet: A web-based resource for a the study of
physiologic signals, IEEE, 2001; 20 (3) p. 70–75.
[7] S. Hans-Gorge, “Wavelets and signal processing; an application-based introduction,”
Newyork: Springer, 2005.

International Journal of Electronics and Communication Engineering Technology (IJECET), ISSN 0976 –
[8] Andr´e E. Aubert, Bert Seps and Frank Beckers, “Heart Rate Variability in Athletes”,
Laboratory of Experimental Cardiology, School of Medicine, K.U. Leuven, Leuven,
Belgium, Sports Med 2003; 33 (12): 889-919.
[9] Malpas SC, Neural influences on cardiovascular variability: possibilities and a pitfalls. Am J
73
Physiol. 2002; 282, p. H6–H20.
[10] Serhat Balcioglu, MD, Ugur Arslan, MD, Sedat Türkoglu, MD, Murat Özdemir, MD, and
Atiye Çengel, MD - Heart Rate Variability and Heart Rate Turbulence in Patients with Type
2 Diabetes Mellitus With Versus Without a Cardiac Autonomic Neuropathy - Am J Cardiol
2007;100:890–893
[11] Chua KC et al. Cardiac state diagnosis using higher order spectra of a heart rate variability.
J Med Eng Technol. 2008; 32(2):145-55.
[12] Shivajirao M. Jadhav, Sanjay L. Nalbalwar and Ashok A. Ghatol, “Performance Evaluation
of Multilayer Perceptron Neural Network Based Cardiac Arrhythmia Classifier”,
International Journal of Computer Engineering Technology (IJCET), Volume 3, Issue 2,
2012, pp. 1 - 11, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[13] Kavita L.Awade, “ECG Signal Processing for Detection and Classification of Cardiac
Diseases”, International Journal of Electronics and Communication Engineering
Technology (IJECET), Volume 1, Issue 1, 2010, pp. 33 - 43, ISSN Print: 0976- 6464,
ISSN Online: 0976 –6472.

Analysis of hrv to study the effects of tobacco on ans among young indians

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