Measurement 43 (2010) 1119–1126

Contents lists available at ScienceDirect

Measurement

journal homepage: www.elsevier .com/ locate/measurement

Development of an embedded system and MATLAB-based GUIfor online acquisition and analysis of ECG signal

R. Gupta, J.N. Bera, M. Mitra *

Department of Applied Physics, University of Calcutta, 92, APC Road, Calcutta 700009, India

a r t i c l e i n f o

Article history:Received 30 October 2009Received in revised form 28 April 2010Accepted 7 May 2010Available online 31 May 2010

Keywords:ECGSerial communicationGUIMATLABFeature extraction

0263-2241/$ - see front matter � 2010 Elsevier Ltddoi:10.1016/j.measurement.2010.05.003

* Corresponding author. Tel.: +91 033 23508386.E-mail address: madhuchhanda94@rediffmail.co

a b s t r a c t

This paper illustrates a low-cost method for online acquisition of ECG signal for storage andprocessing using a MATLAB-based Graphical User Interface (GUI). The single lead ECG issampled at a rate of 1 kHz and after digitization, fed to a microcontroller-based embeddedsystem to convert the ECG data to a RS232 formatted serial bit-stream. This serial datastream is then transmitted to a desktop Personal Computer at a rate of 19.2 kbps and astate-of-the art developed software stores it automatically in a temporary data file. The ori-ginal ECG data is reconstructed from the digital data set by a conversion formula. The MAT-LAB-based GUI is designed to perform online analysis on the ECG data to compute thedifferent time-plane features and display the same on the GUI along with the ECG signalplot.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Electrocardiogram (ECG) is widely accepted cost effec-tive and non-invasive tool for cardiac investigations in aclinical set-up. It shows the plot of surface bio-potentialscaused due to electrical activity of the heart. Careful con-sideration of this electrical information provides sufficientdetail to identify a number of cardiac abnormalities, someof which can lead to chronic disease and ultimately death.In 1887 A.D. Weller first published his work on human ECGrecording. However, W. Einthhoven is considered as thepioneer of standardizing the ECG lead system followedtoday.

ECG data acquisition and automated processing hasbeen one of the thrust areas of research over the last fewdecades. The importance of ECG monitoring find wideapplication in the areas like health-care, sports, military,space-programmes, and home-care services. With theadvancement of communication technologies, backed bylow-power, intelligent, complex processing chip design, itis possible to monitor a patient from remote point. Remote

. All rights reserved.

m (M. Mitra).

monitoring techniques were initially adopted for IntensiveCare Units (ICU) in hospitals, but later implemented on el-derly people, handicapped or patients with chronic dis-eases after they are released from hospitals. Theadvantages of remote monitoring are enhanced freedomof the patient to lead normal life, ability to monitor a num-ber of patients distributed in an area from a centralizedlocation.

Following Einthhoven’s study, the preliminary ap-proach towards of ECG transmission utilized public tele-phone lines, largely based on Frequency Modulation (FM)and Frequency Division Multiplexing (FDM) techniques.The first step in modern Tele-cardiology, with introductionof computers in communication and data processing wasdigitization of the ECG signal [1–3]. Currently PublicSwitched Telephone Network (PSTN) lines with high-speedmodems are still used in developing nations like India forECG signal telemetry. From 1980 onwards, cellular net-works and satellite links were increasingly used in imple-menting ‘mobile’ Tele-cardiology systems [4,5].

Biopotential readout circuits are the most importantpart of an ECG signal acquisition system, not only due toweak values of the signal itself, but also the environmentand the apparatus in which the measurement is done. A

1120 R. Gupta et al. / Measurement 43 (2010) 1119–1126

special biopotential amplifier is used for boosting the weakECG signal. Some essential characteristics of such amplifi-ers are, high Common Mode Rejection Ratio (CMRR) to re-ject interference from mains, low-noise for high signalquality, ultra-low power dissipation for long-term powerautonomy, configurable gain and filter characteristics thatsuit the needs of different biopotential signals under differ-ent applications. An instrumentation amplifier (IA) is themost critical building block of the analog front-end read-out, since it determines the CMRR and noise level [6–11].In addition, it is the most power-consuming block of theanalog signal acquisition circuit. Another IA approach useCurrent Balancing (CB) technique, where the input stageacts as transconductance amplifier [12–15]. One advantageof CB technique is that it eliminates the errors introducedby non-matching of resistors (as in the conventional IA)to achieve high CMRR, and provides low output. Othermethods include Switched-capacitor, Chopper-Modulationtechniques [16–18]. Use of sigma-delta analog to digitalconverters (ADC) for direct ECG acquisition is reported[19].

Automated ECG analysis using different techniques hasevolved to assist the cardiologists in detecting abnormali-ties. ECG signal processing principally involves two areas,QRS detection and, ECG classification for disease identifica-tion. Single lead ECG waveform analysis includes waveshapes (morphology), spectra and repeatability of the car-diac cycle. Multiple lead processing algorithms utilize addi-tional information from other leads. QRS detection formsthe basis of most ECG analysis algorithms, particularly forarrhythmia monitoring [20–22]. Simple QRS detection algo-rithms are based on one of the methods like derivative, fil-ter-banks, wavelets, mathematical morphology andcorrelation [23,24]. More sophisticated approaches use Hid-den Markov Model, syntactic approaches etc. [25–28].

With the increased use of microcontrollers as front-endprogrammable devices, Personal Computer (PC)-baseddata acquisition systems have gained immense popularityover the last decade or more. Microcontrollers are the pre-ferred choice in low-cost data acquisition systems due totheir low-power, programmable, cheap and high-speedfeatures. The sensor output is amplified before digitizationand finally a microcontroller generates the communicationprotocol for transmission to a standalone PC. This enables adirect connection between the ‘compact’ front-end and theHost. In many configurations, the microcontroller ‘talks’with the host to operate under different ‘command’. Thisfeature makes the data acquisition system entirely flexibleand operable from a PC [29–33].

MATLAB is universally accepted as one of the mostpowerful data processing platform. Its connectivity withmany advanced programming languages (like C, Java, VB)and availability of a wide range of toolboxes make it pop-ular among the scientific and research community. MAT-LAB-based GUI application in data acquisition system(DAS) is reported [34–36].

The present work is aimed to develop a simple, costeffective online ECG signal acquisition system for furtherdata processing. An 8051-based dedicated embedded sys-tem is used for converting the digitized ECG into a serialdata, which is delivered to a standalone PC through

‘COM’ port for storage and analysis. Serial link is preferredto minimize cable cost and interference effects as in case ofparallel link. The developed MATLAB-based GUI facilitatesa user to control the operations on the entire system.

2. Materials and methods

The system is developed as modular and the entirework is divided into two major objectives. First one is, todevelop an accurate ECG signal acquisition system to col-lect online ECG data. This consists of ECG source, analogsignal conditioning unit for amplification, ADC for digitiza-tion, then a microcontroller-based embedded system forserial streaming of those data using RS232 protocol. Thedeveloped MATLAB based software provides an efficientway to collect these serial data and automatically store intext files. The second job is to develop a software, whichwould provide a user-friendly interface to the user so thateasy operations can be performed by a semi-skilled profes-sional. In addition, visual display plot for the ECG signal aswell as analysis results are displayed on the GUI. A blockschematic of the entire system is shown in Fig. 1.

2.1. Development of ECG signal acquisition system

The objective of this unit is to acquire the analog ECGsignal in digitized form in a PC for storage and further anal-ysis. This is achieved by a standalone embedded systembased hardware acquisition unit synchronized with MAT-LAB software for automatic data storage in files. The hard-ware acquisition unit consists of an analog signalconditioning circuit (including filter, instrumentationamplifier), an ADC, a microcontroller, and a TTL-RS232 le-vel converter as the main ICs. ECG signal is applied at theinput of signal conditioner. Output of this signal condi-tioner is fed to the ADC input, which provides 8-bit datato the microcontroller unit. The microcontroller convertsthe ADC output into a serial bit-stream for delivery to thePC through its serial port.

The real challenges for accurate ECG signal acquisitionare amplification of the low-amplitude (±3 to 5 mV) ECGsignal for digitization, and after final acquisition, recon-struction of the original signal from digitized values.

A block diagram of the analog processing circuit isshown in Fig. 2. The low-voltage signal is amplified in threesuccessive stages, using OPAMPs LF-353 and OP-07. Theobjective is to offer a high CMRR and the final output isDC-shifted with +2.5 V to avoid the negative half of theamplified signal at the input of ADC. The 8-bit ADC 0804is sampled at every 1 ms interval by the microcontroller,and each sample is converted into a RS232 formatted dataand streamed to the PC at a rate of 19.2 kbps to ensure nodata loss.

The MATLAB-based GUI is designed to receive a prede-fined number of data bytes through the COM port in inter-rupt mode, and automatically store it in a temporary datafile. To automate this serial acquisition and storage, princi-ple of ‘event driven programming’ is utilized. The tempo-rary data file stored values in respect of digital equivalentof ADC input, i.e., in the span of 0–255.

AFG (Agilent 33220A)

Analog Processing

(Voltage divider + Instrumentation Amplifier + DC

bias)

+ ADC

8051 micro-controller

+ Level

converter +

9 pin D-connector

PC with MATLAB-based GUI

ECG signal 8-bit

Digital data

RS232 cable

Fig. 1. Block schematic of the developed system.

AFG Voltage Divider

High CMRR instrumentation

amplifier

ADC Level shifter

+5V

Fig. 2. Analog processing circuit for the ECG signal.

Fig. 3. Signals at AFG output and after amplification, with and without DC bias.

R. Gupta et al. / Measurement 43 (2010) 1119–1126 1121

However, to facilitate the analysis, a reconstruction ofthe signal is performed by using a conversion formula gi-ven below:

AnalogðmVÞ valueðVaÞ

¼ ½Digital valueðDÞ—digital equivalent of bias voltage� � 19:6Total amplification achieved in Amplifiers

Fig. 4. Performance of acquisition system under automatic acquisition mode.

1122 R. Gupta et al. / Measurement 43 (2010) 1119–1126

In our case, the circuit parameters reduced the formulato:

Va ¼ 19:6� ½D� 128�=569

The computation of the scale factor is guided by magni-tude of total amplification achieved in the analog process-ing (569) and the DC shift (+2.5 V) offered to the ADC input.Finally, after reconstruction, the ECG signal is stored in aseparate data file.

2.2. Development of online ECG signal processing software

This processing software provides a preliminary guidefor ECG interpretation based on time-plane analysis andfeature extraction from the stored ECG data. This is supple-mented by displaying plots of the reconstructed signal andthe R–R plot, which assists in visual inspection of the signalby a professional or cardiologist.

The starting point of ECG signal processing is accuratedetermination of R-peaks in the record dataset. A simplederivative based approach is followed for the QRS detec-tion. This is achieved by computing Lagrange five-pointinterpolation formula for derivatives, given as

f 00 ¼1

12hðf�2 � 8f�1 þ 8f 1 � f2Þ

Squaring of second derivative yields positive sharppeaks containing the R-peaks. First, the largest peak is de-tected among the dataset. From this point, a window (ofwidth 70 ms) search is performed on left and right on thedataset to find the maximum R-point. Three such succes-sive R-points are determined to calculate the R–R intervaland also constancy of heart rate is checked. Next, the base-line detection is done in the TP segment of the dataset. Theother points like Q-peak, Q-offset, S-peak, S-offset, T-offset,T-peak and T- end are found by window search methodwith appropriate width, starting from one R-peak.

The algorithm is developed considering maximum pos-sible deviations of the waveform from normal ECG in theECG signal like absence of Q, S waves or, inverted T wavesetc. After determining the characteristic points (P, QRS, T)the following time-plane features are computed:

1. QRS width, 2. QT segment width, 3. ST segmentwidth, 4. Corrected QT segment width.

All the time-plane extracted features are displayed inindividual static text boxes on the GUI along with displayplot for the ECG signal.

3. Testing and results

Two modules of the developed system, viz., ECG serialacquisition and signal processing are separately tested, be-

R. Gupta et al. / Measurement 43 (2010) 1119–1126 1123

fore coupled for their complete operation. Testing of thedeveloped system has been performed on synthetic ECGby simulating it from an Arbitrary Function Generator(AFG) (Model Agilent 33220A). For this purpose ECG datafiles were used from MIT-PTB database under PhysioNet[37]. PhysioNet (www.physionet.org) website is foundedby National Institute of Biomedical Imaging and Bioengi-neering (NIBIB) and National Institute of General MedicalSciences (NIGMS), under US department of Human Healthand Human Services. The site offers collection on differentprerecorded physiological signals. The MIT-PTB database isavailable in PhysioBank under physioNet, and offers digitalrecordings of different physiological signals and relateddata for use by the biomedical research community. Phys-ioBank currently includes databases of multi-parametercardiopulmonary, neural, and other biomedical signalsfrom healthy subjects and patients with a variety of condi-tions with major public health implications, including sud-den cardiac death, congestive heart failure, epilepsy, gait

0 0.5 1 1.5 2

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

time

0 0.5 1 1.5 2time

mV

sign

alm

V si

gnal

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

a

b

Fig. 5. Illustration of R-points determi

disorders, sleep apnea, and aging. PTB, the National Metrol-ogy Institute of Germany, has provided this compilation ofdigitized ECGs for research, algorithmic benchmarking orteaching purposes to the users of PhysioNet. The ECGswere collected from healthy volunteers and patients withdifferent heart diseases.

In the present work, for ECG signal generation, a pre-cisely cut single lead R–R interval data points for normalpatients from MIT-PTB data files is downloaded into thevolatile memory of the AFG, which continuously generatedthe ECG signal by repeating the R–R data points. As thesampling frequency of the ECG PTB database is 1 kHz, thefrequency of the generated signal is determined from thenumber of data points in the R–R data set, and is accord-ingly set from the front panel switches of the AFG. Tomatch the PTB – data files sampling rate, the ADC is sam-pled at a rate of 1 kHz. The minimum value (peak-to-peak)of the synthetic ECG generated from the AFG model canbe ±10 mV, which is greater that the ECG signal le-

2.5 3 3.5 4 4.5 5 (sec)

2.5 3 3.5 4 4.5 5 (sec)

Lead-I plot

Lead VI plot

nation for two individual leads.

Table 1Test results of the signal processing algorithm on ECG database.

Patient file ID Lead I Lead II Lead v1

(a) Sensitivity figure for R-points detection0465 100 100 1000487 100 100 1000470 100 100 1000504 100 96.67 1000502 100 100 1000513 100 100 1000466 100 100 1000468 100 100 1000533 100 100 93.33

Patientfile ID

Lead I Lead II Lead v1

Algorithm Actual Algorithm Actual Algorithm Actual

(b) QRS width calculation (in millisecond)0463 116 116 101 92 182 1500464 112 81 94 99 134 1030465 117 87 99 114 177 1150466 93 86 109 102 125 990468 113 95 98 94 143 1040470 80 89 139 108 125 1230481 150 120 131 110 85 920500 126 110 111 108 105 1000502 119 106 100 93 135 113

(c) QT interval calculation (in millisecond)0463 484 456 419 444 465 4120464 448 418 441 445 421 4180466 394 386 432 399 392 3470468 390 390 408 397 313 3560470 350 388 429 408 340 4000481 432 429 453 405 403 3920500 455 441 464 420 393 3810502 428 400 411 392 372 400

1124 R. Gupta et al. / Measurement 43 (2010) 1119–1126

vel ±5 mV. A voltage divider is used to bring the signal levelwithin ±3 mV. After the DC shift of +2.5 V, the variation ofthe ECG at the input of ADC is found to be within 0–3.30 V, which satisfies the input range (0–5 V) of ADC(0804). The signal shapes of ECG at AFC output, Amplifiersoutput and after DC bias is shown in Fig. 3. A detailedbandwidth study of the amplifier and the serial link is re-ported [38], where the reliability of the link is tested byfirst designing the GUI for starting (and terminating)acquisition of serial data by providing separate push-button, with standard waveforms generated from functiongenerator.

After reliability of the serial link is ensured, the serialacquisition with synthetic ECG input is applied on the sys-tem. A screenshot of digital data file, reconstructed datafile and plot is shown in Fig. 4. The software is designedto accept a predefined fixed 7168 (=14 � 512 bytes of inputdata buffer) incoming data bytes save automatically in atemporary file. The value of 7168 is selected to ensure thatat least 7–8 R-peaks in the stored dataset. The initial plot isgenerated from this file, showing the digital equivalent ofthe signal in a scale of 0–255.

If this initial plot is considered sufficient to ensure thenormal cardiac condition of the patient, the user can skipthe signal processing part of the ECG and delete the tempo-rary file unless it is to be preserved for future purpose. Adigital plot of the signal along with the ECG data file as a

screen snapshot is shown in Fig. 5. For the ECG signal pro-cessing, the MATLAB-based GUI-algorithm is separatelytested on PTB data file to ensure its reliability of R-pointsdetection and other characteristic points. A standard per-formance index of the algorithm for detecting R-peaks ofECG is described by ‘sensitivity’ figure [39], given as:

Se ¼ TP=½TPþ FN�

where TP, called True – Positive gives a R-point found cor-rectly by the algorithm, and FN, called False – Negativegives missed R-points. Table 1a shows some result of thealgorithm tested on MIT-PTB data files with 60,000 datasamples per lead for normal patients. In each case, thealgorithm was tested for accurately determining succes-sive 30 R-peaks. For the given table, obtained average accu-racy is 99.62%. Table 1b and c give some test results fordetermination of QT interval and QRS width with noisy sig-nal from PTB database for normal patients. The ranges forthe calculated features are:

QRS width: 80–120 ms, QT interval: 390–440 ms fornormal patients.

Accurate determination of R-peaks on two differentleads in a PTB data file is also illustrated in Fig. 5.

Once the QRS detection is ensured, the rest part of thealgorithm is developed testing with serial data. The GUIis trialed with more than 100 ‘Normal’ data leads for Malepatients. Fig. 6 shows the digital data plot, reconstructeddata plot, extracted time-plane features on the GUI.

4. Discussion

The objective of this work is to develop a low-cost on-line ECG signal capturing system and data analysis. Theterm ‘low cost’ is used considering the only cost associatedwith the data acquisition hardware as shown in Fig. 2. Thehardware acquisition block is a microcontroller-basedstandalone unit, with instrumentation amplifier, ADC, RS232 level converters as its peripheral ICs. The cost of thisentire hardware consists of cost of components (ICs, resis-tors, capacitors, cables, connectors etc.) which are verycheap (USD 5), PCB making and labour (USD 25). In addi-tion, a power supply is required which costs USD 10. Thusthe total cost of the hardware unit is USD 40 (approx).

Popular commercial varieties of DAS cards (fromAdvantech, NI, Data Translation) offer PCI/USB acquisitionsystems which could be used as possible alternative forthe hardware part of the system. A comparative statementof cost of the developed embedded system with similaralternatives is given in Table 2.

5. Conclusion

In this paper an approach for online ECG signal acquisi-tion and analysis system is reported. No trial on humanbeing has been performed yet since the system is still un-der development. The functionality of the system compo-nents are tested with ECG data from MIT-PTB data base.The results obtained are satisfactory. The chief contribu-tions in the paper are microcontroller-based ECG signalacquisition hardware and data acquisition and processing

Fig. 6. Developed GUI with signal plots and processed ECG features.

Table 2Comparative cost statement of the developed system with similar DAScards.

Make/manufacturer Model/partnumber

Unitcost

National Instruments NI USB-6009

USD 312

National Instruments NI 9227 USD1100

Advantech PCI-1713-AE

USD 515

Advantech USB-4702 USD 207Data Translation Inc. DT 9810 USD 310Data Translation Inc. DT 301 USD 765ECG acquisition system developed by us USD 40

R. Gupta et al. / Measurement 43 (2010) 1119–1126 1125

software developed in MATLAB. The capability of the exist-ing system can be extended to accept 12-lead ECG systemwith simultaneous (multiplexed) data collection, for whichthe microcontroller should sample the ADC at 12 kHz toensure 1 kHz sampling for each channel. Accordingly,speed of the serial transfer to PC should be upgraded(greater than or equal to 120 kbps) to match this high-speed ADC sampling to ensure no loss of data.

Use of serial link for data transmission to the PC pro-vides an opportunity to transfer ECG data from remote pa-

tients through wireless digital communication technique.The existing system can be modified to interface with awireless transceiver, with the data decoding function per-formed by the embedded system. The decoded digital datawill be transferred to the host PC through serial (RS232)link. The MATLAB-based processing software can be up-graded to an ECG classification system. The processed out-put, in the form of a brief report can be sent back to thepatient site through the communication link.

Acknowledgement

The described work has been facilitated by the use Agi-lent make 33220A Arbitrary Function Generator for thepurpose of synthetic ECG signal generation. The instru-ment is purchased under World Bank assisted TechnicalEducation Quality Improvement Programme (TEQIP).

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