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Extraction of qrs complexes using automated bayesian regularization neural network
 

Extraction of qrs complexes using automated bayesian regularization neural network

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    Extraction of qrs complexes using automated bayesian regularization neural network Extraction of qrs complexes using automated bayesian regularization neural network Document Transcript

    • International Journal of Advanced Research in Engineering and Technology (IJARET), IN 0976 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH ISSN– 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME ENGINEERING AND TECHNOLOGY (IJARET)ISSN 0976 - 6480 (Print)ISSN 0976 - 6499 (Online) IJARETVolume 3, Issue 2, July-December (2012), pp. 37-42© IAEME: www.iaeme.com/ijaret.htmlJournal Impact Factor (2012): 2.7078 (Calculated by GISI) ©IAEMEwww.jifactor.com EXTRACTION OF QRS COMPLEXES USING AUTOMATED BAYESIAN REGULARIZATION NEURAL NETWORK Nilesh Parihar, Ph. D*, Scholar, Department of ECE, J.N.U., Jodhpur, Rajasthan, Dr. V. S. Chouhan, Department of Electronics and Communication Engineering, M. B. M. Engineering College, Jodhpur, Rajasthan, IndiaABSTRACTAn efficient algorithm for the detection of QRS complexes in 12 lead ECG is presented in thispaper. The algorithm is developed in MATLAB with standard CSE – ECG data base.Preprocessing is done by using Kaiser-window for minimizing the noise interference anddifferentiator for baseline drift removal. Bayesian regularization neural network is used to learnthe characteristics of QRS complex to detect R peak. This algorithm yields high detectionperformance with detection rate of 98.5% sensitivity is 98.41% and positive predictivity of98.6%.1. INTRODUCTIONECG is a tool that is widely used to understand the condition of the heart. ECG signal is theelectrical representation of the heart activity. In ECG different type of noise commonlyencountered artifacts included as a power line interference, electrode contact noise, motionartifacts, base line drift, instrumentation, electrosurgical noise generated by electric devices [1].Baseline drift is another important parameter to be suppressed for correct detection of QRScomplex. Many researchers have worked on development of methods for reduction of baselinedrift by using Kalman filter, cubic-spline, moving average algorithm and Chebyshave filters. 37
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976– 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEMEV.S. Chouhan and S.S. Mehta developed an effective algorithm for baseline drift removal usingleast squares error correction and median based correction. [2].For automatic ECG data monitoring various implementations have been done previously withMulti level Perceptron and backpropagation training. It is a supervised learning algorithm, inwhich a sum square error function is defined, and the learning process aims to reduce the overallsystem error to a minimum [3, 4]. The network has been trained with moderate values of learningrate and momentum. The weights are updated for every training vector, and the training functionis terminated when the sum square error reaches a minimum value. Also few problems generallyoccur during neural network training that is, over fitting, early stopping and slow processing. Foreffective training, it is desirable that the training data set be uniformly spread throughout theclass domains [5, 6]. In this algorithm automated regularization training function is used toimplement the optimal regularization in an automated fashion for the detection of R peaks. Thenetwork with input layer consists of nodes and subsequent hidden layers [7, 8]. The neurons areprocessed with the standard sigmoid activation function in this paper.2. METHODOLOGY2.1 Filter DesignIn order to attenuate noise and remove the baseline drift we design and implement a FIR bandpass filter with Kaiser Window. The cut off frequency of filter is 0.5 – 40 Hz and order is 7. ଶ଴ ୪୭୥൫௦௤௥௧ሺ௡∗௥ೞ ሻ൯ ିଵଷ ݇ܽ݅‫ = ݓ݋݀݊݅ݓ ݎ݁ݏ‬ భర.ల൫೑ೞ – ೑೛ ൯ ೑The resulting ECG signals are stable.2.2 Bayesian Regularization Neural NetworkIn this algorithm we use a training algorithm which consistently produces networks with goodgeneralization. This method for improving generalization contains the size of the networkweights and is referred to as regularization. 38
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976– 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME Fig. 1 feed forward Neural NetworkIn this technique the data is divided into three subsets. The first subset is the training set, whichis used for computing the gradient and updating the network weights and biases. The secondsubset is the validation set. The error on the validation set is monitored during the trainingprocess. The validation error normally decreases during the initial phase of training, as does thetraining set error. However, when the network begins to over fit the data, the error on thevalidation set typically begins to rise. When the validation error increases for a specified numberof iterations, the training is stopped, and the weights and biases at the minimum of the validationerror are returned. The test set error is not used during training, but it is used to compare differentmodels. It is also useful to plot the test set error during the training process. If the error in the testset reaches a minimum at a significantly different iteration number than the validation set error,this might indicate a poor division of the data set [3, 8].In that once the network weights and biases are initialized, the network is ready for training withproper network inputs p and target outputs t.In Automated Regularization (trainbr) it is desirable to determine the optimal regularizationparameters in an automated fashion. A possible step towards this process is the Bayesianframework. The use of Bayesian regularization function is a combination with Levenberg-Marquardt training process [9].The trainbr algorithm generally works best when the network inputs and targets are scaled so thatthey fall approximately in the range [-1, 1]. If the inputs and targets do not fall in this range, wecan use the function mapminmax to perform the scaling.The algorithm is said to be converged if the sum squared error (SSE) and sum squared weights(SSW) are relatively constant over several iterations. ே 1‫ = ݁ݏ݉ = ܨ‬෍ሺ݁௜ ሻଶ ܰ ௜ୀଵ 39
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976– 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME3. ALGORITHM FOR DETECTION1. Load ECG database files case by case as shown in fig (2a)2. Implement FIR bandpass filter with Kaiser Window as shown in fig (2b).3. Differentiate the output of step 2. These outputs pass through a moving average integrator for form a proper shape and desired level as shown in fig (2c).4. The output of the above step is passed through the neural network, which is having P as an input. The training function is to train feed forward neural network with Bayesian regularization function having target T with a defined learning rate µ=0.05 and number of epoch=4. Further, in this step mapminmax function is used.5. Then the output of mapminmax is trained and simulated with the input. After simulation we get the stable and desired output.6. We apply the threshold condition to detect and mark the R–wave as shown in fig (2d).4. Graphical Results of R-peaks Input ECG signal (MO1122A VF) Input ECG signal (MO1122A VF) 2000 2000 0 0 -2000 -2000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Band Pass Filter output Band Pass Filter output 2000 2000 0 0 -2000 -2000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 " - Neural Network After Training - " " - Neural Network After Training - " -0.8192 -0.9404 -0.8194 -0.9406 -0.8196 -0.9408 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 ==== R - Peak ==== ==== R - Peak ==== 2000 2000 0 0 -2000 -2000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 40
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976– 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME Input ECG signal (MO1122AVF) Input ECG signal (MO1122A VF) 2000 2000 0 0 -2000 -2000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Band Pass Filter output Band Pass Filter output 2000 2000 0 0 -2000 -2000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 " - Neural Network After Training - " " - Neural Network After Training - " -0.899 -0.816 -0.817 -0.9 -0.818 -0.901 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 ==== R - Peak ==== ==== R - Peak ==== 2000 2000 0 0 -2000 -2000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Fig 2 a. Input ECG, b. Filtered output, c. Neural Network output, d. Detection of R peaks5. TESTING RESULTSIn this paper Bayesian regularization neural network is used to learn the characteristics of QRScomplex to detect R peak on the standard CSE database. Bayesian regularization gives very goodresults. Table shows the actual number of QRS complexes (R peaks), number of R peaksdetected, true positive (TP), false negative (FN), and false positive (FP) detection for entire CSE-ECG library dataset-3. Each ECG record of the dataset is of 10 sec duration sampled at 500samples per second, thus giving 5000 samples. The table also shows the detection rate (DR),positive predictivity (+P) and sensitivity (Se): - Table.1. QRS-detection resultsActual no. True False False Detection Positive Sensitivity of QRS Positive Negative Positive Rate Predictivity Secomplexes TP FN FP DR +P 17760 17477 283 32 98.5 % 98.6 % 98.41% 41
    • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976– 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME 6. CONCLUSION The algorithm developed in MATLAB is implemented, with ANN based on Bayesian regularization function, for detection of QRS-complexes. The algorithm is rigorously tested on entire CSE-ECG dataset-3, which includes an exhaustive range of morphologies and vast variety of cases. The resultant values are shown in Table 1 with significantly low values of FP and FN and excellent values of DR, +P and Se. The suitability of the algorithm for this purpose is conspicuously evident.7. REFERENCES1. V.S. Chouhan, S.S. Mehta, “Total Removal of Baseline Drift from ECG Signal”, IEEE Proceedings of International Conference on Computing: Theory and Applications, ICCTA–2007, ISI, Kolkata, India, 0-7695-2770-1/07, pp. 512-515, March 5-7, 2007.2. Manpreet kaur, Birmohan Singh, “Comparisons of different approaches for removal of baseline wonder from ECG signal”, 2nd international journal of computer aplication 2011.3. F. Dan Foresee and martin t. Hagan, gauss-Newton approximation to bayesian learning, School of Electrical and Computer Engineering Oklahoma State University Stillwater.4. Leong Chio ln Wan Feng, Vai Mang, Mak Peng Un, “QRS Complex detector using artificial neural network”, university of Macau, China.5. Yu Hen Hu, J. Thompkins, Joes L. Urrusti, valtino X. afonso, “Applications of artificial neural network for ECG signal detection and classification”, journal of Electro cardiology voll. 26 supplement.6. S. Issac Niwas, R. Shantha Selva kumara, Dr. V. sadasivam, “Artificial neural network based automatic cardiac abnormalities classification”, Proceedings of the sixth international conference on computational intelligence and multimedia application, ICCIMA05, 2005, IEEE.7. Qiuzhen Xue, Yu Hen Hu, J Tompkins, “Neural network based adaptive matched filtering for QRS detection”, IEEE transaction on biomedical engineering, vol.39, no.04, April 1992.8. Matlab the language of technical computing 7.7.0 (R2008b), September 17 2008.9. Kuryati kipli, Mohd Saufee Muhammad, Masniah Wan Masr, “Performance of Levenberg – Marquardt backpropagation for full reference hybrid image quality matrix”, proceeding of the international multi conference of Engineering and computer scientists 2012, vol 1, IMECS 2012, Hang Kong.10. Alireza behrad and karim faez, “New method for QRS – wave reorganization in ECG using MART neural network”, Seventh Australian and newzeland intelligent information systems conference, 18-21 Nov. 2001, Perth, west. Australia. 42