Comparative Analysis of QRS Detection Algorithms and Heart Rate Variability Monitor Implemented on Virtex-4 FPGA

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1 10 Comparative Analysis of QRS Detection Algorithms and Heart Rate Variability Monitor Implemented on Virtex-4 FPGA Srishti Dubey, Kamna Grover, Rahul Thakur, AnuMehra, Sunil Kumar Dept. of Electronics and Communication Engineering Amity University, Noida, India ABSTRACT Heart disease is gaining epidemic proportions in the world. In India, on average, four people die of heart attack every minute, before any emergency service can intervene. Given the vitality of sudden deaths caused by heart attacks, a circuit is designed to detect the state of the human heart by calculating the heart rate following the detection of the QRS ECG waveform peaks. In the paper, the two most commonly used QRS detection algorithms Murthy and Rangaraj & Pan and Tompkins, are used.the algorithmsare first implemented in MATLAB and then the performance comparison is done. The detection architecture is designed in Verilog HDL to calculate the heart rate. FPGA implementation is performed further. Designed code is simulated in MATLAB 2012 and Xilinx ISE 13.2.The design is synthesized and assembled in Xilinx Virtex-4 FPGA on ISE 13.2 platform. Keywords- MATLAB, Verilog HDL, ECG, QRS, FPGA, RAM, ALU, Heart Rate I. INTRODUCTION During each 60 second interval, the average blood cell pumped makes a round trip through the body's arteries and it beats 60 to 120 times, depending on the state of the heart. Due to plaque buildup in arteries, blood flow is blocked and the heart faces difficulty in circulating blood. This clogging causes a myriad of heart problems, from chest pain to heart attack. Electrocardiography (ECG) is a graphical representation of the electrical activity of the heart wave recorded. The waveform has P, QRS and T complexes. R represents the exact time of the highest amount of electrical impulse present in the ventricle. P wave represents the activation of the upper chambers of the heart, the artery, while the QRS complex and T wave represents the excitation of the ventricles of the lower chamber of the heart. Among the three main components P, QRS and T wave, using the detection of the R wave is the rule being adopted for signal analysis. The signal is usually corrupted by baseline drift, interference powerline, moreover, P and T wave also correspond to the noisy parts and must bemitigated. Much research work is dedicated to make thesignal without noise for accurate QRS detection.[1] - [2] Fig.1 ECG Signal with peaks indicated QRS Detection algorithms In the literature, by far many algorithms have been implemented for the detection of QRS. Popular techniques for the peak detection are based on first and second derivative of the ECG signal. Balda [6] used the first and second derivative both and suggested searching the values which are above threshold, in the weighted summation of both derivatives. Ahlstrom and Tompkins Algorithm [7] adds the absolute values of the first and second derivative. Two thresholds, primary and secondary, are used. Earlier adopted algorithms [9] and [10] were based on only the first derivative; [11], [12] and [13] were based on the amplitude and first derivative. The two most commonly used algorithms Murthy &Rangaraj[5] and Pan & Tompkins[8] have been used in this paper for peak detection and heart rate calculation. This paper is organized as follows. Section II covers MATLAB Implementation of Murthy and Rangaraj Algorithm. Section III covers the MATLAB Implementation of Pan and Tompkins Algorithm. Section IV presents the comparisonalgorithms. Section V covers the calculation of Heart Rate in MATLAB.Section VI

2 11 presents detection of heart rate using Verilog HDL and FPGA implementation. Section VII shows simulation results and synthesis. Section VIII concludes the paper. II. MATLAB IMPLEMENTATION OF MURTHY AND RANGARAJ ALGORITHM (a) (b) Murthy and Rangaraj Algorithm Murthy and Rangaraj Algorithm, a QRS detection algorithm, is based upon the squaring, taking derivative and applying Moving Average Filter on ECG Signal. Block diagram in Fig 2 shows the steps of algorithm. (c) (d) ECG signal Derivative Squaring and Scaling operation Moving average Filtering Fig.2 Block Diagram of the algorithm for its graphical representation The equation used for calculating a single peak at the QRS complex is given below N gi n = x n i + 1 x n i 2 (N n + 1) i=1 (1) where x (i) is the ECG signal, and N is the width of a window within which the first-order differences are calculated, squared, and weighted by the factor (N - n + 1). The weighting factor decreases linearly from the current difference of the difference N samples earlier in time and provides a smoothing effect. Smoothing is carried out by a Moving Average Filter on M points.the complete equation for detecting the peak without any ripples is: M 1 j =0 g(n) = 1 M j2 (N i+1)(2) Waveforms N i=1 x n i j + 1 x n i Waveforms corresponding to each step, obtained from the implementation of algorithm in MATLAB, are shown in Fig 3. (e) Fig.3Waveforms of the implemented algorithm (a) ECG input signal (b) Derivative (c) Squaring and scaling (d)filtering(moving Average) (e) QRS Peak Detection III. MATLAB IMPLEMENTATION OF PAN AND TOMPKINS ALGORITHM Pan and Tompkins Algorithm This algorithm performs the analysis of slope, width and amplitude of QRS complexes. Various filters and methods likebandpass filter, derivative, squaring, Moving windowintegration filter are used. The steps are shown in the schematic below: ECG signal Band pass filter Differenti ation Squaring and scaling operation Fig.4 Block Diagram of the steps of algorithm Moving window integrator The output of low pass filter is related to the input with the equation: ecg i = 2ecg1 i 1 ecg1 i [x i x i 6 + x i 12 ](3)

3 12 The output of high pass filter comes in equation: ecg i = x i 16 1 [ecg3(i 1) + x(i) + x i 32](4) 32 The derivative procedure suppresses the low-frequency components of the P and T waves, and provides a large gain to thehigh-frequency components that arise from the high slopes of the QRS complex. The algorithm performs smoothing of the output of the preceding operations through a moving-window integration filter as: ecg i = 1 N x(i)](5) Waveforms x i (N 1 + x i (N Waveforms corresponding to each step, obtained from the implementation of algorithm in MATLAB, are shown in Fig 3. Tompkins algorithm. While, according to Murthy and Rangaraj, the input signal does not pass through any filter, instead it directly gets differentiated. After differentiating the signal, it passes through the Moving Average filter. Preferred Algorithm Comparing the two algorithms, Murthy &Rangaraj algorithm is evaluated to be the best, as it provides reduced noise and sharp peak output which is quite difficult to obtain in other algorithm s method. Moreover, Pan and Tompkins algorithm makes the signal undergo band pass filter which adds a sufficient amount of delay in the waveform representation which need to be subtracted for its proper analysis further. Therefore, the algorithm is slow in its operation. Whereas, performing the same operation with less delay and by avoiding usage of unnecessary filter, we can get the correct output in Murthy and Rangaraj Algorithm. (a) (b) V. CALCULATION OF HEART RATE IN MATLAB After the QRS detection, the R-R interval is used for calculating the heart rate.we calculated the heart rate for the four different input ECG signals using both the algorithms. Four different ECG data base, taken from MIT/BIH database, are provided as input to MATLAB as.dat file. Table1. Results of Heart Rate Calculation in MATLAB for different inputs (c) (d) Input ECG Database Heart Rate using Muthy&Ra ngarajalgo rithm Heart Rate using Pan & Tompkins Algorithm Heart Function Ecg3.dat Normal (e) Fig.5Waveforms of the implemented algorithm (a) ECG input signal (b) Band pass filter (c) Derivative (d) Squaring and scaling (e)filtering (Moving Window Integration) (f) QRS Detection IV. (f) ALGORITHM COMPARISON Ecg4.dat Normal Ecg5.dat Tachy cardia Ecg6.dat Brady cardia ECG signal input undergoes low pass and high pass filtering operations before going for derivation in Pan and

4 13 VI. VERILOG AND FPGA IMPLEMENTATION To implement ECG signals on Virtex-4 FPGA, RTL schematic was designed using Verilog-HDL. (a)verilog Implementation The architecture used for Verilog Implementation is shown in Fig6. There are seven modules consisting of Input, Output, RAM, Data Bus,ALU, Pointer(Ptr), Program Counter(PC). Out of various RAM locations, only A and B is used here. For heart rate calculation in Verilog HDL, ecg3.dat file is provided as input. (b)heart Rate Calculation in Verilog To calculate the heart rate, the following approach has been used. Since RR interval varies, average heart rate should be estimated by determining the number of RR intervals in 10 sec time strip and multiplied it by 6. It is a commonly used medical technique to calculate heart rate from ECG Signal. (c)fpgaimplementation Hardware implementation of the Verilog Code is performed on Virtex4 FPGA kit. The FPGA implementation is successful. Therefore, the device is ready to be implemented on chips. PC Input Ptr D A T A B U S ALU RAM ----A B OUT Fig.7FPGA Implementation Successful VII. SIMULATION RESULTS AND SYNTHESIS Fig.6 Architecture of proposed device Internal Operation: The Verilog Design implemented, has four modules. Top module instantiates three other modules. First module, count_pulse, counts the number of pulses in every 10 clock cycles and stores the value in RAM location A. Second module named RAM, stores the data in RAM locations whenever Read signal is enabled. Firstly, data is stored in RAM location A. It gets transferred into RAM location B whenever new data has to be stored again in the location A i.e. after every next 10 clock cycles. Third module, shift_alu, performs the arithmetic calculations on the data stored in RAM, to find out the heart rate. Fig 8 shows the RTL Schemtaic of our design. It consists of three main modules named as count_pulse, ram andshift_alu as described earlier. Fig.8 RTL Schematic

5 14 Expanded view of the schematic shows the internal connections of each module. Each module is simulated separately to check the functioning. Waveforms obtained in each simulation process are shown further. Fig12 represents the output waveform of count_pulse module. Fig.12 Simulation result of count_pulsemodule Fig 13 shows the output waveforms obtained by simulation of ram module. Fig.9 Expanded view of count_pulse module Simulation result of ram module Fig.13 Fig 14 represents the simulation result of shift_alumodule. Fig.14 Simulation result of shift_alu module Fig.10 Expanded view of ram module Fig 15 shows the final simulation result of top module. Output data represents the calculated heart rate. It is 60 in the case when input provided corresponds to ecg3.dat file. Fig.15 Simulation result of toplevel module VIII. CONCLUSION Fig.11 Expanded view of Shift_alu module In the paper proposed we compared the heart rate of human being using MATLAB and Verilog HDL. To achieve our goal, we first uploaded MIT/BIH data file on MATLAB as input and performed the QRS detection, calculation of heart

6 15 rate, using both of the Murthy and Rangaraj, Pan and Tompkins Algorithm. We compared both the algorithms to find the best one. We performed band pass filtering derivative, filtering, squaring and scaling operations to detect the peaks of two consecutive ECG signal and calculated the R-R interval. Using the R-R interval and applying the formula, we calculated the heart rate. We took four different ECG Signal inputs, heart rate was normal for two cases while two other cases revealed Tachycardia and Bradycardia. For hardware implementation, we designed architecture for the process in Verilog HDL and performed FPGA implementation using Virtex4 FPGA. The heart rate obtained by implementing the MATLAB code was approximately same as that obtained using Verilog HDL language. Slight variation in the results of MATLAB and Verilog is allowed. Murthy & Rangaraj algorithm provides reduced noise and sharp peak output which was quite difficult to obtain in other algorithms likepan & Tompkins method. Further, ECG Signal can be used to determine the effect of emotions on heart rate and hardware can be designed in future. This will help the people to understand that what effect does emotions have on our heart rate which could be highly beneficial in medical science. REFERENCES [1] G.M. Friesen, T. C. Jannett, M.A. Jadallah, S.L. Yates, S.R.Quint,and H.T. Nagle, A comparison of the noise sensitivity of nine QRS detection algorithms, IEEE Trans. Biomed. Eng., vol. 37, pp , Jan [2] B-U Kohler, C Hennig, and R Orglmeister, The principles of software QRS detection, IEEE Eng. in Medicine and Biology Magazine, vol. 21, pp , Jan [3] Sameer Palnitkar: 'Verilog HDL, A guide to digital design and synthesis', Pearson, Second Edition, IEEE compliant. [4] RANGA YYAN R.: 'Biomedical Signal Analysis, A Case Study approach', IEEE Press Series in Biomedical Engineering. [5] IVATURI S. N. MURTHY, MANDAYAM R. RANGARAJ Homomorphic Analysis and Modeling of ECG Signals IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. BME-26, NO. 6, JUNE 1979 [6] BALDA R. A. (1977): 'The HP ECG analysis program' in V ANBEMNEL 1. H. and WILLEMS J.L. (Ed): 'Trends in Computer-Process Electrocardiograms, (North Holland), pp [7]AHLSTROM M. L. and TOMPKINS W. J. (1983):'Automated High Speed Analysis of Holter Tapes with Microcomputers', IEEE Trans. Biomedical Engineering,30, pp [8] PAN J., and TOMPKINS S W. J. (1985): A Real- Time QRS Detection Algorithm IEEE Trans. Biomedical Engineering, 32, pp [9]MENRAD A., (1981):"Dual Microprocessor cardiovascular data acquisition, processing and recording system ', Proc. of 1981 Int. Conf. Industrial Electrical Contr. Instrument, pp [l0]hol SINGER W. P. et a!. (1971):'A QRS Preprocessor Based on Digital Differentiation', IEEE Trans. Biomed. Eng., 18, pp [11] MAHOUDEAUX P. M. (1981): Simple Microprocessor based system for on-line ECG analysis, Med. Biological Eng.. Comput., 19, pp [12] FRADEN J., and NEUMAN M. R. (1980): QRS Wave Detection, Med. Biological Engineering Comput., 18, pp [13] GUSTAF SON D., (1977): Automated VCG interpretation studies using signal analysis techniques ", R- 104 Charles Stark Draper Lab., Cambridge, MA. [14] Christos Pavlatos, AlexandrosDimopoulos, G. Manis and G. Papakonstantinou HARDWARE IMPLEMENTATION OF PAN &TOMPKINS QRS DETECTION ALGORITHM