Full Paper                                                           ACEEE Int. J. on Information Technology, Vol. 3, No. ...
Full Paper                                                             ACEEE Int. J. on Information Technology, Vol. 3, No...
Full Paper                                                                 ACEEE Int. J. on Information Technology, Vol. 3...
Full Paper                                                               ACEEE Int. J. on Information Technology, Vol. 3, ...
Full Paper                                                                 ACEEE Int. J. on Information Technology, Vol. 3...
Full Paper                                                                 ACEEE Int. J. on Information Technology, Vol. 3...
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FPGA based Heart Arrhythmia’s Detection Algorithm

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Electrocardiogram (ECG) signal has been widely used
for heart diagnoses .In this paper, we presents the design of
Heart Arrhythmias Detector using Verilog HDL based on been
mapped on small commercially available FPGAs (Field
Programmable Gate Arrays). Majority of the deaths occurs
before emergency services can step in to intervene. In this
research work, we have implemented QRS detection device
developed by Ahlstrom and Tompkins in Verilog HDL. The
generated source has been simulated for validation and tested
on software Verilogger Pro6.5. We have collected data from
MIT-BIH Arrhythmia Database for test of proposed digital
system and this data have given MIT-BIH data as an input of
our proposed device using test bench software. We have
compared our device output with MATLAB output and
calculating the error percentage and got desire research key
point of RR interval between the peaks of QRS signal. The
proposed system also investigated with different database of
MIT-BIH for detect different heart Arrhythmias and proposed
device give output exactly same according to our QRS detection
algorithm.

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FPGA based Heart Arrhythmia’s Detection Algorithm

  1. 1. Full Paper ACEEE Int. J. on Information Technology, Vol. 3, No. 1, March 2013 FPGA based Heart Arrhythmia’s Detection Algorithm Sheikh Md. Rabiul Islam, A. F. M. Nokib Uddin, Md. Billal Hossain, Md. Imran Khan Dept. of Electronics and Communication Engineering Khulna University of Engineering &Technology Khulna, Bangladesh E-mail: robi@ece.kuet.ac.bd,nokib.ece@gmail.com, billal.0709018@gmail.com, imrankhankuet@gmail.com.Abstract- Electrocardiogram (ECG) signal has been widely used a human heart for detection of diseases [6]. To acquire thefor heart diagnoses .In this paper, we presents the design of signal, ECG devices with varying number of electrodes (3–Heart Arrhythmias Detector using Verilog HDL based on been 12) can be used. Multi lead systems exceeding 12 and up tomapped on small commercially available FPGAs (Field 120 electrodes are also available [7].Programmable Gate Arrays). Majority of the deaths occurs The QRS complex is the largest deflection voltage of aboutbefore emergency services can step in to intervene. In thisresearch work, we have implemented QRS detection device 10 - 20 mV but may vary in size depending on age and sex ofdeveloped by Ahlstrom and Tompkins in Verilog HDL. The the human. The amplitude of the voltage of the QRS complexgenerated source has been simulated for validation and tested may also give information on heart disease [8]. Duration ofon software Verilogger Pro6.5. We have collected data from the QRS complex indicates the time for the ventricles toMIT-BIH Arrhythmia Database for test of proposed digital depolarize and can provide information on problemssystem and this data have given MIT-BIH data as an input of conduction in the ventricles as the bundle branch block.our proposed device using test bench software. We have The MIT-BIH [9] arrhythmia database is used forcompared our device output with MATLAB output and performance evaluation based on the study. The MIT-BIHcalculating the error percentage and got desire research key database contains 48 disks, each containing two channels ofpoint of RR interval between the peaks of QRS signal. Theproposed system also investigated with different database of ECG signals for 30 min duration of 24 hours selected from theMIT-BIH for detect different heart Arrhythmias and proposed recordings of 47 individuals. There are also data numbersdevice give output exactly same according to our QRS detection 116.137 QRS complexes in the database [10]. The databasealgorithm. record contains both timing information and class information heartbeat checked by independent experts [11].Index Terms -Verilog HDL, QRS, ECG, MIT-BIH, Heart Xilinx ISE [12],[13] is a software tool produced by XilinxArrhythmia. for synthesis and analysis of HDL designs, which enables the developer to synthesize designs, examine RTL diagrams, I. INTRODUCTION simulate a design’s reaction to different stimuli, and configure Heart arrhythmias occurs when the electrical impulses in the target device with the programmer. The building blockheart that coordinate your heartbeats don’t work properly, were designed, tested and evaluated using the ISE toolcausing your heart to beat too fast, too slow or irregularly available from Xilinx & VeriloggerPro6.5 (TestBencher[1]. Arrhythmias can take place in a healthy heart and be of pro)[14].minimal consequence but they may also indicate a serious This paper is structured as follows. Section II describesproblem that may lead to stroke or sudden cardiac death [2- the QRS detection algorithm. Section III part A describes3]. Heart arrhythmia treatment can often control or eliminate about the Ahlstrom and Tompkins algorithm and section Birregular heartbeats. There are hundreds of different types of for Arrhythmias detection process. In Section IV part Aheart arrhythmias among them common arrhythmias are atrial describes the bus architecture and section B is for describingfibrillation (AF), Premature Atrial Contractions (PAC), Atrial modules. Section V shows simulation and synthesized result.Tachycardia, Atrial Fibrillation, Atrial Flutter, Premature Finally Section VI for conclusion.Ventricular Contractions (PVC), Ventricular Tachycardia (VT),Ventricular Fibrillation. II. QRS DETECTION ALGORITHM Electrocardiogram (ECG) is a diagnosis tool that reported Till now, various methods have been reported by researchersthe electrical activity of heart recorded by skin electrode. for detection of QRS complex [15-18], Algorithms [19-21] areThe morphology and heart rate meditates the cardiac health based amplitude and the first derivative .In [20] a point isof human heart beat [4]. It is a noninvasive technique [5]. classified as QRS candidate when three consecutive pointsAny disorder of heart rate change in the morphological of the first derivative exceed a positive threshold (ascendingpattern is a reading of cardiac arrhythmia, after measure could slope) followed within the next 100ms by two consecutivebe detected by analysis of ECG waveform. Whatever the points which exceed a negative threshold (descending slope).magnitude and duration of the P-QRS-T wave contains useful Algorithms [22] and [23] are based on the first derivativeinformation about the nature of the disease that affects the only. Algorithms [24-26] are based on the first and secondheart in this regard the electric wave is due to depolarization derivatives. Ahlstrom and Tompkins in [24] proposed thatand polarization king of Na + and blood of the body ions the absolute values of the first derivative are smoothed andhuman [5].The ECG signal provides important information of added with the absolute values of the second derivative.© 2013 ACEEE 9DOI: 01.IJIT.3.1. 4
  2. 2. Full Paper ACEEE Int. J. on Information Technology, Vol. 3, No. 1, March 2013Balda [25] suggested searching values exceeding the TABLE I. HEART RATE AND R-R I NTERVAL FOR D IFFERENT HEART ARRHYTHMIA.threshold in a weighted summation of the first and secondderivative, later this method was developed by Ahlstrom andTompkins [24]. Friesen [27] and Tompkins [28] alsoinvestigated similar methods based on sensitivity of QRScomplex to noise. There is several QRS detection methodbased on the digital filter [29-33], filter banks [34-35]. III. METHODOLOGYA. Basic QRS Complexes Detection Algorithm Among the so many algorithms we select first and second functions of a data bus to carry information and address busderivative based scheme developed by Ahlstrom and to determine where it should be sent and a control bus toTompkins [24] the rectified first derivative is calculated from determine its operation .The Bus architecture of the proposedthe ECG: digital system is shown in Figs.1-3. Y0(n)=ABS[X(n+1)-X(n-1)] 3<n<8188 the rectified first derivative is then smoothed: Y1(n)=[Y0(n-1)+2Y0(n)+Y0(n+1)]/4 3<n<8188 The rectified second derivative is calculated: Y2(n)=ABS[X(n+2)-2X(n)+X(n-2)] 3<n<8188 The rectified smoothed first derivative is added to therectified second derivative: Y3(n)=Y1(n)+Y2(n) 3<n<8188 The maximum value of this array is determined and scaledto serve as primary and secondary thresholds: Primary threshold=0.8*max[Y3(n)] 3<n<8188 Secondary threshold=0.1*max[Y3(n)] 3<n<8188 The array of the summed first and second derivative isscanned until a point exceeds the primary thresholds. In orderto be classified as a QRS candidate, the next consecutivepoint must all meet or exceed the secondary threshold. Thatis the threshold and QRS detection algorithm is just like bellow: Fig. 1. Block diagram of the system including data bus. Y3(i) >= primary threshold, and Y3(i+1), Y3(i+2) . . . . . . Y3(i+6) > secondary threshold After detecting the QRS complex we detect the R peakand R-R interval. There are several types of noise (i.e. muscleartifact, electrode motion artifact, white noise etc.) mayhamper the ECG signal as well as the performance also.Performance of first and second derivative based method ismuch better than the first derivative methods [36].B. Arrhythmias Detection For detection of Arrhythmias at first we need to know theheart rate of different Arrhythmias. Bellow table shows theheart rate for different arrhythmias. If the sampling frequencyFs Hz then, Time for one sample Ts= second If R-R interval (in sample) is N samples then, R-R interval (in time) = Ts N second Fig. 2. Block diagram of the system including control bus and After calculating R-R interval we can detect heart internal structureArrhythmia from Table. I. In our system input and output is unidirectional to data bus. Input block take input data from data source and send it IV. PROPOSED ARCHITECTURE to data bus. Output block take data from data bus and showA. Bus Architecture the output data.RAM, register A, register B and ALU are bidirectional that is it can send and receive data from dataA system bus is a single computer bus that connects the bus. In control bus architecture shown in Fig.2, control blockmajor components of a computer system. It combines the determines the operation of rest of the block using control© 2013 ACEEE 10DOI: 01.IJIT.3.1. 4
  3. 3. Full Paper ACEEE Int. J. on Information Technology, Vol. 3, No. 1, March 2013 TABLE II. C ONTROL BUS FROM CONTROL R OM TO DIFFERENT MODULES. Fig. 3. Block diagram of the system including Address bus. “RAM” to save the data permanently.bus except PC and PRO-PC. All other blocks are connected 5. Register A & Bwith control bus. Those connections are unidirectional. These two 16 bit registers are used for to save the dataConnection between PRO-PC and PC represent the internal temporarily & send the data to ALU or RAM.connection.In Fig.3 it shows the address bus architecture. 6. ALURAM, PTR and P block are connected with address bus. The ALU is the arithmetic logic unit which is designed forfunctions of these blocks are described in section IV (B). doing addition, subtraction, shift left, shift right arithmetic calculations. In our proposed design ALUB. Description of Different Modules. can process 16 bit data and save processed value to its In our proposed design we have designed thirteen blocks. internal register and can send it to reg A, B and RAM.All function of those blocks is described below: 7. PTR1. Pro-Program Counter (PRO-PC) PTR inform P block about the initial position of its It is a one kind of clock pulse generator and also a counter. parameter(X, Y0, Y1, Y2, and Y3) of the equation. Output ofIt generates clock pulse for driving the Program counter (PC) PTR is 4bit.from the main clock pulse. In this design it generates a pulse 8. Pwith one positive edge against four main clock pulses. That is P block select the RAM address for each parameterPro-PC force PC to provide same output several times. Output (X(n),Y0(n),Y1(n),Y2(n),Y3(n)).Output of P block is 4 bit.of Pro-PC is 2bit. 9. Output2. Program Counter (PC) It is a register which stands for saving the output data. A program counter is counter which count the clock. The We use 16 bit output register in our proposed design.main objective of this block is control the “control” block. 10. RAMProgram counter take two bit input from Pro-PC and using RAM stands for random access memory. It is volatileclock signal it count 0 to 138.So the output of Program counter memory to store temporary data.RAM is an array of registeris 8bit.At the first time of counting it counts 0 to 12 sequences bank which is accessible (read and write) according to address.to take input six consecutive data sequentially. After that it We use 192 bit RAM which contain 12 memory unit each unitcounts 17 to 138 sequences to process six data and give have 16bit storage capability. From the data bus architectureoutput to output register. If it count 138 sequence it will start shown in Figure.1 we can see that it can send and receivere-counting from 13-138 again and again in order to take two data from Reg. A, B, INPUT, ALU. That is connection betweenconsecutive data input, process it and give output to output RAM and data bus is unidirectional. Table III shown thatregister. location and value of RAM according to the PTR and P block3. Control ROM output. It is the ROM of our system. It take 8bit input from theProgram Counter and give 36 bit output which is called control V. SIMULATION RESULTS & SYNTHESISbus and connected almost all block to control their operation.Block diagram of control bus given in Fig.2 in previous Synthesis is the process of constructing a gate levelsection. Now bellow Table II shows the control bus structure. netlist from a register-transfer level model of a circuit describedTable II. Control Bus From Control Rom To Different Mod- in Verilog HDL. After synthesizing in Xilinx project navigatorules. we got RTL schematic diagram of proposed design which is4. Input shown in Fig.5. Fig.4 shows synthesized RTL schematic of In our system we use 16 bit input register to receive input the top level of our proposed design. Simulation is the processsampled value from the peripheral device & store the data of verifying the functional characteristics of models at anytemporarily. It can also send the data to the block called level of abstraction. To test if the RTL code meets the© 2013 ACEEE 11DOI: 01.IJIT.3.1. 4
  4. 4. Full Paper ACEEE Int. J. on Information Technology, Vol. 3, No. 1, March 2013 TABLE III. ADDRESS O F R AM FOR DIFFERENT PARAMETERS Ahlstrom and Tompkins algorithm. Fig.10, 11 and 12 stands for normal ECG signal, Ventricular Tachyarrhythmia and Atrial fibrillation respectively.In order to measure R-R interval, we take 1100 samples for each signal. For normal ECG signal we use 360Hz sampling frequency and for other we use 250Hz.In Fig.10, R-R interval is 292 samples that is 0.76 (according to equation 1) seconds, so heart rate is approximately 79bpm that indicates it is normal ECG signal according to Table I.Similarly for Fig.11, heart rate is 120 bpm (Beats Per Minute) approximately which clearly indicates that is ventricular Tachyarrhythmia. In Figure.12 we can see that the R-R interval is not constant. As a result it can be concluded, it is atrial fibrillation.functional requirements of the specification shown if all theRTL blocks are functionally correct. To archive this we needto write test bench which generates clk, reset and requiredtest vectors. Figs. 6-9 shows test bench output for real ECGdata input which is listed in Table III. Fig. 6. Output for first eight consecutive input data Fig. 4. Block Diagram of our proposed design Fig. 7. Ninth data input to testbench. Fig. 8. Output for ninth input data. Fig. 5. RTL schematics of our proposed design. For higher accuracy we multiply a gain factor with normalECG data.In this work we used actual ECG records such asMIT100 from MIT-BIH database whereas we have given itas a input to testbench and collect output data as tabulatedin Table IV.There are small deviation between testbench outputand MATLAB output due to unablity to calculate floatingpoint.This deviation is neglegible.In Figs.10- 12 shows by Fig. 9. Output for tenth input data.MATLAB output for QRS complex and R peak detection using© 2013 ACEEE 12DOI: 01.IJIT.3.1. 4
  5. 5. Full Paper ACEEE Int. J. on Information Technology, Vol. 3, No. 1, March 2013 TABLE IV. C OMPARE BETWEEN VERILOG T ESTBENCH AND MATLAB O UTPUT The proposed model is loaded on XILINX FPGA board. It is implemented upon XILINX SPARTAN XC2S150 FPGA board processor. When it is implemented on FPGA board processor then the clock frequency of the processor is 1MHZ. The proposed model has the same power consumption, signal bandwidth and CMOS technology is used on the XC2S150 processor. CONCLUSIONS We have coded the equations of Ahlstrom and Tompkins method using Verilog Hardware Description Language. Try to detect the peak of two consecutive ECG waveform, found the time interval of R-R is detected. Compare with the time interval of R-R of normal cardiac conditions, the abnormalities of heart rate is determined. Some heart arrhythmias depends not only the R-R interval but also P, T wave. Our system can’t detect those kinds of arrhythmias. We use VeriloggerPro6.5 (Test Bencher pro) demo version which has a limitation that it cannot simulate over 1225 line and show more than 26 consecutive outputs correctly. In future we will try to update our system for calculating floating point data and also use licence version of VHDL. REFERENCES [1] Sandoe E, Sigurd B. Arrhythmia–—a guide to clinical electrocardiology. Bingen: Publishing Partners Verlags GmbH, 1991. [2] Goldberger L, Goldberger E. Clinical electrocardiography. Saint Louis: The Mosby Company, 1977. Fig. 10. R peak detection for normal ECG signal [3] Sideris DA, Primary cardiology. Athens: Scientific Editions Grigorios K Parisianos, 1991 (in Greek). [4] J. Pan, W. J. Tompkins, “A real time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. 32, pp. 230–236, 1985. [5] Y.C. Yeha, and W. J. Wang, “QRS complexes detection for ECG signal The Difference Operation Method (DOM),” Computer methods and programs in biomedicine, vol. 9, pp. 245–254, 2008. [6] P.de Chazal, M.O. Duyer, and R.B. Reilly, “Automatic classification of heartbeat using ECG morphology and heart beat interval features,” IEEE Trans. Biomed. Eng. vol. 51, pp. 1196-1206, 2004. [7] P. Zarychta, F. E. Smith, S. T. King, A. J. Haigh, A. Klinge, D. Zheng, S. Stevens, J. Allen, A. Okelarin, P. Langley, and A. Fig. 11. R peak detection for Ventricular Tachyarrhythmia Murray, “Body surface potential mapping for detection of myocardial infarct sites,” in Proc. IEEE Comput. Cardiol., Sep./Oct. 2007, pp. 181–184. [8] P. de Chazal, R.B. Reilly, “A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval feature,” IEEE Trans. Biomed. Eng. vol. 53, pp. 2535-2543, 2006. [9] D. A. Coast, R. M. Stern, G. G. Vano and S. A. Biller, “An approach to cardiac arrhythmia analysis using Hidden Markov Model, “ IEEE Trans. Biomed. Eng., vol. 37, no. 9, Sep. 1990. [10] R. J. Schalkoff, “Pattern Recognition: Statistical, Structural, and Neural Approaches”, JOHN WILEY & SONS, INC., 1992. [11] T. H. Yeap, F. Johnson, and M. Rachniowski, “ECG beat classification by a neural network,” in Proc. Annu. Int. Conf. IEEE Engineering Medicine and Biology Society, 1990, pp. 1457–1458. Fig. 12. R peak detection for atrial fibrillation. [12] Samir palnitkar; ‘Verilog HDL.’ SUNSOFT PRESS 1996.© 2013 ACEEE 13DOI: 01.IJIT.3.1. 4
  6. 6. Full Paper ACEEE Int. J. on Information Technology, Vol. 3, No. 1, March 2013[13] XILINX, Internet site address: http://www.xilinx.com [30] W. A. H. Engelse and C. Zeelenberg, “A single scan algorithm[14] TestBencher pro Internet site address: http:// for QRS detection and feature extraction,” IEEE Comput. www.syncad.com. Cardiology. Long Beach, CA: IEEE Computer Society, pp.[15] M. Benmalek, A. Charef, “Digital fractional order operators 37-42, 1979. for R-wave detection in electrocardiogram signal,” IET Signal [31] P. S. Hamilton and W .J. Tompkins, “Quantitative investigation Processing, vol. 3, no. 5, pp. 381-391, Sept. 2009. of QRS detection rules using the MIT/BIH arrhythmic[16] Q. Z. Xie, Y. H. Hu, and W. J. Tompkins, “Neural-network database”, IEEE Trans. Biomed. Eng., vol. 33, pp. 1157-1165, based adaptive matched filtering of QRS detection,” IEEE 1986. Trans. Biomed. Eng., vol. 39, pp. 317-329, 1992. [32] L. Keselbrener, M. Keselbrener, and S. Akselrod, “Nonlinear[17] J. Pan and W. J. Tompkins, “A real-time QRS detection high pass filter for R-wave detection in ECG signal”, Med. algorithm”, IEEE Trans. Biomed. Eng., vol. 32, pp. 230-236, Eng. Phys., vol. 19, no. 5, pp. 481-484, 1997. 1985. [33] S. Suppappola and Y. Sun, “Nonlinear transforms of ECG[18] Fei Zhang, Yong Lian, “QRS Detection based on multiscale signals for digital QRS detection: A quantitative analysis”, mathematical morphology for wearable ECG devices in body IEEE Trans. Biomed. Eng., vol. 41, pp. 397-400, 1994. area networks,” IEEE Trans. on Biomed. Circuits and Systems, [34] V. X. Afonso, W. J. Tompkins, T. Q. Nguyen and S. Luo, vol. 3, no. 4, pp. 220-228, Aug. 2009. “ECG beat detection using filter banks,” IEEE Trans. Biomed.[19] J. Fraden and M. R. Neumann, “QRS wave detection”, Med. Eng., vol. 46, pp.192-202, 1999. Biol. Eng. Comput., vol. 18, pp 125-132, 1980. [35] Stéphane Mallat, “A Wavelet Tour of Signal Processing”, 3rd[20] P.M.Mahoudeaux,” Simple microprocessor-based system for ed., Academic Press, 2008. on-line ECG analysis, “Med. Bio. Eng. Comput.,vol.19,pp [36] Gary M., Friesen, Thomas C. Jannett, Manal Afify Jadallah, 497-500,1981 Stanford L.Yates, Stephen R. Quint, H. Troy Nagle,” A[21] D.Gustafson, “Automated VCG Interpretation studies using Comparison of the Noise Sensitivity of Noise QRS Detection signal analysis techniques,” R-1044 Charles Stark Draper Algorithms,” IEEE TRANSACTIONS ON BIOMEDICAL Lab.,Cmabridge,MA,1977 ENG.,VOL.37,PP.95-96.[22] A. Menred, “Dual microprocessor system for cardiovascular data acquisition, processing and recording, “in Proc.1981 IEEE AUTHOR BIOGRAPHIES Int. Conf. Industrial Elect. Contr. Instrument.,1981,pp.64-69[23] W.P. Holsimger , “A QRS pre-processor based on digital Sheikh Md. Rabiul Islam received the B.Sc.in Engg. differentiation” IEEE Trans. Biomed. Eng.,vol.BME- (ECE) from Khulna University, Khulna, Bangladesh 18,pp.212-217,1971 in December 2003, and M.Sc. in Telecommunication[24] M.L. Ahlstrom and W J. Tompkins, “Automated high-speed Engineering from the University of Trento, Italy, in analysis of holter taps with microcomputers,” IEEE Trans. Biomed. Eng., vol. BME-30,pp.651-657,Oct.1983 October 2009. He joined as a Lecturer in the department of Elec-[25] BALDA R. A. “The HP ECG analysis program” in tronics and Communication Engineering of Khulna University of VANBEMNEL J. H. and WILLEMS J.L. (Ed): ‘Trends in Engineering & Technology, Khulna, in 2004, where he is currently Computer- Process Electrocardiograms’ , (North Holland), pp. an Assistant Professor in the same department in the effect of 197-205, 1977 2008. He has published 15 Journals and six international confer-[26] R. M. Rangayyan, “Biomedical signal analysis: a case-study ences.. His research interests include Numerical analysis, VLSI, approach”, IEEE Press, 2001. wireless communications , signal & image processing, and biomedi-[27] G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. cal engineering. Quint and H. T. Nagle, “A comparison of the noise sensitivity A. F. M. Nokib Uddin received a B.Sc. Engg. (ECE) of of nine QRS detection algorithms”, IEEE Trans. Biomed. Eng., department of Electronics and Communication Engineering at vol. 37, pp. 85-97, 1990. Khulna University of Engineering & Technology, Khulna_9203,[28] W. J. Tompkins, “Biomedical digital signal processing”, Bangladesh. Prentice-Hall, Upper Saddle River, NJ, 1995. Md.Billal Hossain received a B.Sc. Engg.(ECE) of department[29] M. Okada, “A digital filter for the QRS complex detection,” of Electronics and Communication Engineering at Khulna University IEEE Trans. Biomed. Eng., vol. 26, pp. 700-703, Dec. 1979. of Engineering & Technology, Khulna_9203, Bangladesh. Md. Imran Khan received a B.Sc. Engg. (ECE)of department of Electronics and Communication Engineering at Khulna University of Engineering & Technology, Khulna_9203, Bangladesh© 2013 ACEEE 14DOI: 01.IJIT.3.1. 4

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