This document describes a system for acquiring electrocardiogram (ECG) signals, extracting features from the signals, and analyzing heart rate variability. The system uses Arduino hardware to amplify, filter, and digitize ECG signals. LabVIEW software interfaces with the Arduino to acquire the digital data and save it in file formats for analysis. Biomedical Workbench software then processes the ECG data, extracting features like QRS complexes and heart rate. It also performs heart rate variability analysis using time and frequency domain measures on the extracted RR intervals. The complete system provides a low-cost way to acquire, process, and analyze ECG signals.

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International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 9, Issue 3, May – June 2018, pp. 84–90, Article ID: IJARET_09_03_012
Available online at http://www.iaeme.com/ijaret/issues.asp?JType=IJARET&VType=9&IType=3
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
© IAEME Publication
ECG SIGNAL ACQUISITION, FEATURE
EXTRACTION AND HRV ANALYSIS USING
BIOMEDICAL WORKBENCH
Arjun Singh Vijoriya and Dr. Ranjan Maheshwari
Department of Electronics, Rajasthan Technical University, Kota
ABSTRACT
This Paper contains the complete process of ECG/EKG signal Acquisition from
hardware to its analysis using LabVIEW and Biomedical Workbench. Hardware of ECG
has the amplification, filtering and conversion of analog ECG data to digital by using
Arduino Uno. The acquisition part deal with acquiring the hardware data to analyzable
file format into pc. Here 6-channel ADC in Arduino Uno with LabVIEW interface is used
for conversion. Now the acquired ECG data is processed and analyzed with biomedical
workbench that provides the various features of ECG signal processing. This system is
very easy to implement and cost effective.
Keywords: 2-Lead ECG System, LabVIEW, ECG Signal Processing Tools, ECG
Analysis, Biomedical Workbench.
Cite this Article: Arjun Singh Vijoriya and Dr. Ranjan Maheshwari, ECG Signal
Acquisition, Feature Extraction and HRV Analysis Using Biomedical Workbench,
International Journal of Advanced Research in Engineering and Technology, 9(3), 2018,
pp 84–90.
http://www.iaeme.com/ijaret/issues.asp?JType=IJARET&VType=9&IType=3
1. INTRODUCTION
Electrocardiogram (EKG/ECG) is used to measure and monitor the heart electrical activities
in detail from many years. These electrical details are used to diagnosis the heart conditions.
From centuries to till now there is several advanced hardware and software tools have been
developed for Electrocardiogram signal acquisition and analysis [1].
The ECG/EKG signal is the graphical representation of heart electrical activities in the
form of voltage and current generated during the cardio muscles contraction and relaxation.
The generated voltage/current is very small in magnitude and these could be measure from
the body skin surface by placing the appropriate ECG electrode. The magnitude of this ECG
signal is about few microvolts to 0.5mv. These cardio signals frequency range is between
0.05 to 100 Hertz (Hz).
The electrocardiogram recordings in hospitals are increasing with time. However modern
ECGs produce digital output, but still plain paper is in use to record the ECG data.
Sometimes ECG data of patient become necessary to transfer at another distance place for
analysis and paper based data is too much time consuming and also difficult to have record of

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patient database for long period. So it is requirement of present time to have the data in
digital form in various analysable file formats [2].
We described the complete ECG data acquisition process from hardware to further signal
processing software tools in computer system. Hardware having the several stages from ECG
signals amplification, filtering, conditioning to analog to digital conversion and software
having the real time plotting of ECG signal in LabVIEW and save the plotted data for further
usable digital file formats like .txt, .tdms, .tdm, .xlsx for required time duration. The Further
biomedical workbench uses the files to analysis of the ECG data; this is having ECG feature
extraction and Heat Rate Variability Analyzer tools as the core requirement of recent study
for analysis. The main objective of the project work is to design the ECG system which can
help the researchers and doctors to acquire and analyse the ECG data in detail with easy and
cost effective tools in very less time.
2. SYSTEM DESIGN OVERVIEW
This Complete ECG System can be understood by simple block diagram having different
stages.
Figure 1 System Block Diagram
3. ECG HARDWARE
This ECG hardware contains the signal amplification; signal filtering, signal conditioning and
ADC. Since the ECG signal is millivolt signal, to amplify it we uses the double stage national
instrumentation amplifier with gain of 50 using IC INA126u at first stage and LMC8081 at
second stage. Higher gain at first stage could cause low CMRR therefore the gain is applied
to successive stages. The Next stage of this hardware 1.3 volt offset to the amplified ECG
signal because the Arduino uno having unipolar ADC that clips the negative part of the signal
therefore to avoid the clipping of the signal 1.3 volt offset is applied to the signal using the
op-amp LMC6081 with unity gain.
Figure 2 A front view of ECG hardware connected with arduino uno
Above complete setup produced the amplified and shifted clear ECG waveform. Since
this signal is in analog form, to make this analog signal into digital format it is connected this
to Arduino uno which is having the 6-channel, 10-bit resolution analog to digital convertor.
4. DATA ACQUISITION
The Arduino uno board is interfaced with PC through the LabVIEW software for data
acquisition from the ECG hardware to personal computer and LabView has the different
Arduino modules that can acquire the data continuously at specific sampling rate. This
acquire digital data from Arduino uno is stored in .tdms file using write measurement files
module of LabView which is having the various functions to store the data file like different

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file extensions and number sample to store and time limit to store the file. The Labview VI
for data acquisition with Arduino Uno Interface is shown in figure (3).
Figure 3 Labview VI for data acquisition with Arduino Uno Interface
This complete setup produces real time ECG signal on computer screen and records this
signal into a file format that is used for further analysis.
5. ECG SIGNAL PROCESSING AND FEATURE EXTRACTION
Biomedical Workbench of National Instrumentation is used for ECG signal processing since
it is having ECG feature extractor and heart rate variability analyzer with various filtering and
plotting functionality.
ECG feature extractor of Biomedical Workbench is used for filtering and feature
extraction of ECG signal. It is able to import ECG signals in different file formats. This tool
having robust integrated feature extraction algorithms to detect ECG signal features, such as
the QRS Complex, P wave and T wave, total number of beats, Iso level mean and standard
deviation , ST level mean and standard deviation, PR Interval mean and standard deviation,
QT interval mean and standard deviation. It can save extracted ECG features into TDMS file
and also can take print. It transfers Calculated RR interval data to Heart Rate Variability
Analyzer of Biomedical Tool Kit for HRV analysis. Below figure shows the feature
extraction of 8:46 minute ECG data taken from the above hardware circuit.
Figure 4 ECG Feature Extractor of Biomedical Workbench
After applying inbuilt signal processing methods, it produces following results with
histogram plot of 8:46 minute ecg data.
Figure 5 Heart rate histogram and ECG features

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6. HEART RATE VARIABILITY AND ANALYSIS
Heart rate variability (HRV) is actually a physiological occurrence which implies shifts
within time interval or space in between a single beat of the cardiovascular system to the
subsequent. The inter beat interval (IBI) is most likely the time in between one R-wave to the
upcoming. That is acquired in milliseconds. The inter beat interval is extremely variable
quantity throughout any period of time. Heart rate variability relies upon three features
particularly physical, emotional and psychological. Therefore, the resulting structure of heart
rate variability can be described as a joint result from the facets mentioned above. [3]
Figure 6 Measurement of HRV from ECG Signal [2]
Heart rate variability analyzer of biomedical workbench provides the all parameters and
statistics with plot of RR intervals presented in ECG data shown in figure (7). By using this
tool we can analyze the HRV using different analysis methods like Poincare Plot, FFT
Spectrum Measures, AR Spectrum Measures, STFT Spectrogram, Gabor Spectrogram,
Wavelet Coefficient, DFA Plot and Recurrence Map.
6.1 Statistical Parameters
As it's just unveiled that HRV is in fact composed by using a number of RRIs. Time domain
HRV factors are therefore likely to relate to variations in HRV, in other words, the difference
in RRIs. Most significant parameters have been put up in the table.
Table 1 Statistical results calculated by Heart Rate Variability Analyzer
Statistical
Parameter
Description
Results for 8:46
minute data
RR mean Mean of RR intervals 666 ms
RR std., Standard Deviation RR intervals 34 ms
Heart rate mean Mean of heart rate 90 bpm
Heart rate std Standard Deviation of heart rate 5.5 bpm
RMSSD
Root Mean Square of Successive RR
intervals
25 ms
NN50
Number of successive RR interval having
difference greater than 50 ms.
25
pNN50 It is the portion of NN50 in all RR intervals. 3.2
RR triangular
index
9
TINN 129.2 ms
Below figure shows the plot generated by Heart rate Variability Analyzer of National
Instrumentation between the extracted RR intervals and number similar RR intervals (Count)
for recorded 8.46 minute data.

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Figure 7 RR Interval Vs count, plot generated by HRV analyser.
All those time domain factors are recognized to possess connection to the health issue of
the person and in fact utilized in the recognition of the health problem of sufferers. For
instance, those factors will probably have greater value if RRIs tend to be extremely varying
that will due to suffering sinus disorder, premature ventricular contraction and atrial
fibrillation. Whenever the RRIs possess lesser variation, e.g., third degree AV block7, those
factors possess reduced value [4].
Hence by acquiring the difference in the parameters, it may be possible to identify the
sudden variations within the body of sufferer just in time that is of good advantageous.
6.2. Poincare Plot
Poincar´e plot is just a return chart that can help to conduct graphical study of information.
We can even add an ellipse into the plot structure by calculating descriptors SD1 (Standard
Deviation1), SD2 (Standard Deviation 2) and SD1/SD2 ratio to analyze the reports
quantitatively [5].
The Poincare plot provides a very useful visible interaction into the R-R data files by
portraying both the short as well as long-term changes within the recording. Study on
Poincare plots can be carried out from a quick visual check on the structure of the attractor
(such as butterfly structure), that is used to identify the signal. Over chronic renal failure
sufferers this method has turned out to be helpful to examine the emergency forecast in the
existence of coronary disorder. Still, the evaluation and calibration of such qualitative
categories are complicated as they are very highly subjective. An actual quantitative
evaluation on the HRV attractor shown over the Poincare plot could be produced by changing
it in an ellipse. For this overall performance testing, the SD1, SD2 and region of ellipse
utilized as analysis variables. These types of analysis variables have a variety of explanations
in other analysis reports [6]. The foremost proper explanations are defined below:
6.2.1. SD1: Standard Deviation1
This is actually the standard deviation (SD) of the entire instantaneous beat to beat N-N time
interval variability (SD1 or ellipse's minor axis) [6].
6.2.2. SD2: Standard Deviation 2
This is actually the standard deviation (SD) of the entire long-term N-N time interval
variability (SD2 or ellipse's major axis) [6].
6.2.3. Area of Ellipse
This is actually the range of region covered up by ellipse. This is determined by performing
the multiplication on π, SD1 and SD2.
Figure 8 Poincare plot and its generated SD1 and SD2 over 8:46 minute data by Heart rate Variability
Analyzer

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6.3. FFT Spectrum Measures
In an attempt to examine the sympathovagal equilibrium, scientific study on the frequency
domain has become essential. The PSD commonly utilized to acquire these types of details.
For a particular duration of the RR series, the PSD will be calculated very first and some
other analysis methods choose to follow. Depending on the proven fact that HRV is varying
in accordance with the movements of the person [7]. As things are stated in [8], factors which
are found within Low Frequency LF: 0.04 Hz ≤ LF < 0:15 Hz and within High Frequency
HF: 0.15 Hz ≤ HF < 0.4 Hz bands are actually the main aspects those are directly related to
the health problem of the individual.
Below is the FFT spectrum analysis result generated by Heart Rate Variability Analyzer
conducted on previous data at the following setting of frequency band, FFT and window
respectively. VLF: 0 - 0.04 Hz; LF: 0.04 - 0.15 Hz; HF: 0.15 – 0.4 Hz, Interpolation rate: 2
Hz; Frequency bins: 1024 and window sample length: 1024; overlap: 50%.
Table 2 Numerical data of FFT spectrum analysis of normal 8:47 minute ecg signal with and without
using windows using Heart Rate Variability Analyzer.
Windows
selection
VLF
Power
(ms2)
LF
Power
(ms2)
HF
Power
(ms2)
VLF
(%)
LF (%) HF (%)
LF norm
(n.u.)
HF
norm
(n.u.)
LF/HF
Corres-
ponding
Plot
None 230 630 171 22 61 17 71.2 19.4 3.7 (a)
Hanning 420 770 227 30 54 16 71.5 21.1 3.4 (b)
Hamming 410 760 223 29 55 16 71.5 21 3.4 (c)
Blackman-Harris 470 790 234 31 53 16 71.6 21.3 3.4 (d)
Exact Blackman 470 790 234 31 53 16 71.6 21.3 3.4 (e)
Blackman 470 790 235 31 53 16 71.6 21.3 3.4 (f)
Flat Top 630 930 272 34 51 15 72.5 21.2 3.4 (g)
4 Term B-Harris 510 820 243 33 52 15 71.7 21.3 3.4 (h)
7 Term B-Harris 600 890 262 34 51 15 72.3 21.2 3.4 (i)
Low Sidelobe 540 840 249 33 51 15 71.9 21.3 3.4 (j)
Figure 9 Plots generated by above FFT spectrum measure between PSD (s^2/Hz) and Frequency (Hz)

7. Arjun Singh Vijoriya and Dr. Ranjan Maheshwari
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7. CONCLUSION
Hardware and LabVIEW software both together creates real time ECG waveform. This real
time ECG waveform stored in a digital file at required time duration and sampling rate.
Biomedical workbench of National Instrumentation have very efficient tools for ECG signal
processing, feature extraction and heart rate variability analysis. All the analysis techniques
described above are very advanced and extensively used by researchers. This complete
process from getting signal to its heart rate variability analysis is very easy and can be used
for self-diagnosis.
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