Implementation of Derivative Based QRS Complex Detection Methods

Transcription

1 Implementation of Derivative Based QRS Complex Detection Methods Rahul Kher Electronics & Comm. Engg. Dept, G H Patel College of Engg. & Tech, Vallabh Vidyanagar, India Dipak Vala and Dr. Tanmay Pawar Electronics Engg. Dept, BVM Engg. College, Vallabh Vidyanagar, India Dr. V. K. Thakar Electronics & Comm. Engg. Dept, A D Patel Institute of Tech, New Vallabh Vidyanagar, India Abstract In this paper QRS complex detection algorithms based on the first and second derivatives have been studied and implemented. The threshold values for detecting R-peak candidate points mentioned in previous work have been modified for accuracy point of view. The derivative based QRS detection algorithms have been found not only computationally simple but exceptionally effective also on variety of ECG database that includes highly noisy and arrhythmic ECG signals. This is indicated by an average detection rate of over 98% obtained through the modified threshold values even for the challenging ECG test sets. Keywords-ECG, QRS complex, Derivative-based algorithms, Detection rate, Wavelets, Morphology, PCA, ANN, SVM I. INTRODUCTION Electrocardiogram (ECG) is the most widely studied and processed bioelectrical signal, as it reflects the electrical activity of the most important human physiological organ, the heart. The signal processing of an ECG signal dates back in early 96s and it is one of the most explored areas in the field of biomedical engineering. The QRS complex is the most prominent and peculiar segment of an ECG signal. The detection of the QRS complex is the most important task in automated ECG signal analysis. For example, in []-[4] ST-T analysis is done after detection of QRS waves. In [5]-[9], body position movements and motion artifacts in ambulatory ECG are analyzed after detection of QRS locations. In [] adaptive recurrent filter is proposed, which requires a QRS detector for implementation. A similar kind of adaptive filter is applied in [] for separating motion artifacts from ECG, which requires QRS detector in preprocessing steps. In [2]-[4] non-linear PCA (NLPCA) based ECG pattern recognition and classification is implemented in which initially it requires accurate recognition of QRS wave. In [5]-[2] ECG beat and arrhythmia classification based on artificial neural network (ANN) and support vector machine (SVM) are implemented in which QRS detection is required. Once the QRS complex has been identified, a more detailed examination of ECG signal, including the heart rate, the ST segment, etc., can be performed. Pan and Tompkins [2] have first devised an effective yet simplistic algorithm for real-time QRS detection. Since then a lot of efforts have been devoted by the researchers in this direction and they have come out with varieties of algorithms for detecting the QRS complex. In [22]-[24] authors have used first and second derivative-based approach for QRS detection from an ECG signal. Wavelet transform (WT) has been extensively used by the research community for analysis and feature extraction of ECG signals. Many types of wavelets like Daubichies, Biorthogonal spline, Symlets and even newly developed wavelets with different scales, decomposition and threshold levels have been reported in [25]-[3] for QRS detection. Wavelet transform is a very promising technique for timefrequency analysis. By decomposing the signal into elementary building blocks that are well localized both in time and frequency, the WT can characterize the local regularity of signals. This feature can be used to distinguish ECG waves from noise, artifacts and baseline drift. The local maxima of the WT modulus at different scales can be used to locate the sharp variation points like QRS wave of ECG signals [25]. In [32]-[33] Hilbert transform has been applied for detecting the onset and offset of QRS complex. The Hilbert transform is very useful for signal demodulation without knowing the carrier frequency. If we consider a QRS complex as a modulated waveform, the beginning and end of the QRS complex envelope calculated using the Hilbert transform coincide with the QRS onset and offset respectively. Mathematical morphology, which is based on set operations, provides an approach to the development of nonlinear signal processing operators that incorporate shape information of a signal. In mathematical morphological operations, the result of a set transformed by another set depends on the shapes of the two sets involved. The shape of a signal is determined by the values that the signal takes on. The shape information of the signal is extracted by using a structuring element to operate on the signal [34]. References [34]-[36] explore various morphological operators for QRS complex detection. Chauhan and Mehta in [37] have developed an adaptive/dynamic threshold based QRS detection algorithm, which is further enhanced and modified in [38]-[39]. The above mentioned algorithms provide adequate amount of accuracy in QRS detections but at the expense of heavy computational complexities and long time durations.

2 Therefore, the first and second derivative-based QRS detection algorithms have been studied and implemented in this paper, which overcome the above mentioned drawbacks of other algorithms. They have been found computationally simple, less time consuming though very effective and accurate. The MIT/BIH ECG database has been used for experiments. As many as 25 test set of the ECG waves of different morphologies including the challenge 2 test set A, B and C have been simulated on the Matlab environment. It has been observed that the accuracy rate of QRS detection algorithm based on first and second derivative is increased by adopting the variable thresholds instead of fixed threshold mentioned in [22]. Section II and III describe the implementation details of algorithms based on first derivative and second derivative, respectively, along with experimental results followed by conclusion in section IV. II. ALGORITHM BASED ON FIRST DERIVATIVE A. Algorithm Based on First Derivative and Amplitude Threshold The concept for this algorithm has been adopted from [22]. The first derivative, Y(n), is calculated at each point of the ECG signal X(n): Y( n) = [ X( n+ 2) 2 X( n 2) + X( n+ ) X( n )] (3) The slope threshold (Th3) is calculated as.7*max (Y) [22]. The first derivative array was searched for points which exceed the slope threshold. The first point that exceeds the slope threshold, i.e. Y(i) > Th3 is taken as the QRS onset point. For this algorithm also instead of fixed threshold (Th3), a variable threshold (Th4) is used following the previously mentioned criterion of rate of QRS detection. Fig. 2 shows the detected QRS waves for MIT/BIH challenge 2 test set B using the above mentioned algorithm using Th4. For most of the ECG data sets the value of Th4 was set to.6*max(y). The performance of this algorithm with Th4 is found superior than that obtained with of Th3. III. ALGORITHM BASED ON SECOND DERIVATIVE The absolute values of the first and second derivatives, Y(n) and Y(n), of the ECG signal, X(n) are calculated as in [22], as follows: Y( n) = X( n+ ) X( n ) (4) Y( n) = X( n+ ) X( n ) () The first derivative array is then searched for points which exceed a constant threshold: ( Th) Yi () >.5 (2) Then the next three consecutive derivative values Y( i + ), Y( i +2 ) and Y( i + 3 ) must also exceed this constant threshold. If the above conditions are met, then point i can be classified as a QRS candidate. In this work, we have modified the value of the threshold and termed it as (Th2). The value of Th2 has not been kept fixed; instead it has been varied between.8 to.35, depending upon the rate of detection of QRS complex in ECG signal as previously suggested in Pan- Tompkins algorithm []. For most of the cases the value of Th2 was found to be around.3. This is done for reliable detection of the QRS waves, which cannot be obtained with the constant threshold (Th=.5) defined in [22]. Fig. shows the simulation results of the above mentioned algorithm for MIT/BIH challenge 2 test set B. The average detection rate for this algorithm has been found to be 98.36% with Th2 as against 95.26% obtained with Th, see Table. Thus modifying the value of the constant threshold (Th) to the variable threshold (Th2) has improved the detection rate of QRS waves. B. Algorithm Based on First Derivative Only The first derivative Y(n) is calculated for each point of the ECG signal X(n), using the formula specified in [22] as: Y( n) = X( n+ 2) 2 X( n) + X( n 2) (5) These two arrays are scaled and then summed: Y2(n) =.3*Y(n) +.* Y(n) (6) This array is scanned until a threshold is met or exceeded: Y2(i) >=. (Th5) (7) Once this occurs, the next eight points are compared to the threshold. If six or more of these eight points meet or exceed the threshold, the criterion for identification of a QRS candidate is met. The fixed threshold value (Th5 =.) in (7), as suggested in [22], is too much coarse for some ECG signals, particularly the ones in which T waves are having equal or greater amplitude levels (test set B47). This is the reason for poor performance of this algorithm, which has been upgraded by selecting the variable threshold (Th6). The value of Th6 has been varied between.25 to.55 for improving the accuracy of the QRS detection. Fig. 3 depicts the experimental results for this algorithm applied on the same ECG test set. Tables I to III summarize the experimental results for the three algorithms implemented on Matlab environment. The ECG database includes the MIT/BIH arrhythmia test sets (tape no. to 27) and challenge 2 test sets A, B, and C (A9, B47, B52 and C9). A total of 6 data sets and 92 beats of ECG signals were tested for evaluating these algorithms.

3 Figure. Experimental Results for First derivative and Amplitude Threshold based algorithm: ECG signal (B47), First Derivative of the signal, Detected QRS waves Figure 2. Experimental Results for the Algorithm based on First Derivative Only: ECG signal (B47), First Derivative of the signal, Detected QRS waves (d) Figure 3: Experimental Results for the Algorithm based on Second Derivative: ECG signal, Absolute First derivative of the signal, Absolute Second derivative of the signal, (d) Detected QRS waves.

4 TABLE I. RESULTS OF FIRST DERIVATIVE AND AMPLITUDE BASED QRS DETECTION ALGORITHM Th Th2 Th Th A B B52 2 C Total TABLE II. RESULTS OF FIRST DERIVATIVE BASED QRS DETECTION ALGORITHM Th3 Th4 Th3 Th A B B52 2 C9 2 Total TABLE III. RESULTS OF SECOND DERIVATIVE BASED QRS DETECTION ALGORITHM Th5 Th6 Th5 Th A B47 2 B C9 2 Total IV. CONCLUSION Three different algorithms namely- first derivative and amplitude threshold based, first derivative only based and first and second derivative based- for QRS complex detection have been implemented on MIT/BIH ECG data base. Total 6 ECG data sets have been taken into count that includes the arrhythmia beats and challenge 2 test sets A, B and C in order to include the variations in the ECG wave morphologies. Total 576 beats have been tested through these algorithms; out of which QRS waves have been correctly detected for 5637 beats i.e % with variable thresholds (Th2, Th4 and Th6), whereas with fixed thresholds (Th, Th3 and Th5) suggested in [22] this rate has been found to be 94.85%. Comparing the three algorithms, the first derivative only based algorithm has an edge over the other two algorithms, as it attained the highest detection rate. The first and second derivative based algorithm performs poorly due to the high threshold value (Th5 =., used in [22]). But, using the variable and flexible values of thresholds definitely improves the QRS detection performance. REFERENCES [] José García, Leif Sörnmo, Salvador Olmos, and Pablo Laguna, Automatic Detection of ST-T Complex Changes on the ECG Using Filtered RMS Difference Series: Application to Ambulatory Ischemia Monitoring, IEEE Transactions on Biomedical Engineering, Vol. 47, No. 9, September 2. [2] Franc Jager, George B. Moody, and Roger G. Mark, Detection of Transient ST Segment Episodes During Ambulatory ECG

5 Monitoring, Computers And Biomedical Research 3, (998). [3] Gu-Young Jeong and Kee-Ho Yu, Design of Ambulatory ECG Monitoring System to detect ST pattern change, SICE-ICASE International Joint Conference, Oct. 8-2, 26, Bexco, Busan, Korea. [4] A. Smrdel and F. Jager, Automated Detection Of Transient ST- Segment Episodes In 24 H Electrocardiograms, Med. Biol. Eng. Comput., 24, 42, [5] T. Pawar, S. Chaudhuri, and S. P. Duttagupta, Analysis of Ambulatory ECG Singal, in 28 th IEEE EMBC, New York City, New York, USA, Aug.-Sept 26, pp [6] T. Pawar, N. S. Anantakrishnan, S. Chaudhuri, and S. P. Duttagupta, Impact Analysis of Body Movement in Ambulatory ECG, in 29 th IEEE EMBC, Lyon, France, Aug. 27, pp [7] T. Pawar, S. Chaudhuri, and S. P. Duttagupta, Body Movement Activity Recognition for Ambulatory Cardiac Monitoring, IEEE Transactions on Biomedical Engineering, Vol. 54, no. 5, pp , May 27. [8] T. Pawar, N. S. Anantakrishnan, S. Chaudhuri, and S. P. Duttagupta, Transition Detection in Body Movement Activities for Wearable ECG, IEEE Transactions on Biomedical Engineering, Vol. 54, no. 6, pp , June 27. [9] T. Pawar, N. S. Anantakrishnan, S. Chaudhuri, and S. P. Duttagupta, Impact of Ambulation in Wearable-ECG, Springer Link Annals of Biomedical Engineering, Vol. 36, No. 9, September 28, pp [] Nitish V. Thakor and Yi-Sheng Zhu, Applications of Adaptive Filtering to ECG Analysis: Noise cancellation and Arryhthmia Detection, IEEE Transactions on Biomedical Engineering, Vol. 38, no. 8, pp , August 99. [] S. Chaudhuri, T. Pawar and S. Duttagupta, Ambulation Analysis in Wearable ECG, Springer, 29, ISBN [2] N. Maglaverasa, T. Stamkopoulosa, K. Diamantarasb, Costas Pappasa, and Michael Strintzisb, ECG Pattern Recognition And Classification Using Non-Linear Transformations And Neural Networks: A review, International Journal of Medical Informatics, Vol.52, Issue, pp 98-28, 998. [3] C. Papaloukas, D. Fotiadis, A. Likas, L. Michalis, An Ischemia Detection Method Based On Artificial Neural Networks, Artificial Intelligence in Medicine 24 (22) [4] Stamkopoulos T, Diamantaras K, Maglaveras N and Strintzis M, ECG Analysis Using Nonlinear PCA Neural Networks For Ischemia Detection IEEE Transactions on Signal Processing, Vol. 46, no., pp , Nov [5] G. Vijaya; Vinod Kumar and H. K. Verma, Artificial neural network based wave complex detection in electrocardiograms, International Journal of Systems Science, Vol. 28, Issue 2, pp , February 997. [6] Vijaya G, Kumar V and Verma H K, ANN-based QRS-complex Analysis of ECG, Journal of Med Eng Technol. Jul-Aug 998; 22(4):6-67. [7] Dipti Patra, Manab Kumar Das and Smita Pradhan, Integration Of FCM, PCA and Neural Networks For Classification Of ECG Arrhythmias, IAENG International Journal of Computer Science, Vol. 36 No. 3, February 2. [8] S. S. Mehta and N. S. Lingayat, ECG Pattern Classification Using Support Vector Machine, Advances in Pattern Recognition, [9] S. S. Mehta, and N. S. Lingayat, Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram, IAENG International Journal of Applied Mathematics, Vol. 36, No. 2, May 27. [2] Elif Derya Ubeyli, ECG beats classification using multiclass support vector machines with error correcting output codes, Digital Signal Processing Vol. 7, Issue 3, May 27, pp [2] Jiapu Pan and Willis J. Tompkins, A Real-Time QRS Detection Algorithm, IEEE Transactions on Biomedical Engineering, Vol. Bme-32, No. 3, March 985. [22] Gary M. Friesen, Thomas C. Jannett, M.A. Jadallah, S. L. Yates, S. R. Quint and H. Troy Nagle, Comparison Of The Noise Sensitivity Of Nine QRS Detection Algorithms, IEEE Transactions on Biomedical Engineering, Vol. 37, No.3, Jan. 99. [23] B.-U. Kohler, C. Hennig, and R. Orlgmeister, The principles of software QRS detection, IEEE Engineering in Medicine and Biology Magazine, vol. 2, no., pp , 22. [24] Natalia M. Arzeno, Zhi-De Deng, and Chi-Sang Poon, Analysis of First-Derivative Based QRS Detection Algorithms, IEEE Transactions on Biomedical Engineering, Vol. 55, No. 2, February 28. [25] Cuiwei Li, Chongxun Zheng, and Changfeng Tai, Detection of ECG Characteristic Points Using Wavelet Transforms, IEEE Transactions on Biomedical Engineering, Vol. 42, No., January 995. [26] Shubha Kadambe, Robin Murray, and G. Faye Boudreaux-Bartels, Wavelet Transform-Based QRS Complex Detector, IEEE Transactions on Biomedical Engineering, Vol. 46, No. 7, July 999. [27] Yanli Zheng, Guangshu Hu, QRS Complex Detection by the Combination of Maxima and Zero-crossing Points of Wavelet Transform, Proceedings of the 2 th IEEE EMBC, HongKong, Oct 29-Nov 998, pp [28] L. Szilagyi, Z. Benyo, M. Szilagyi, A. Szalvecz and L. Nagy, Online QRS Complex Detection using Wavelet Filtering, Proceedings of the 23 rd IEEE EMBC, October 23-25, 2, Instambul, Turkey, pp [29] Xiuyu Zheng, Zhen Li, Linlin Shen and Zhen Ji, Detection Of QRS Complexes Based On Biorthogonal Spline Wavelet, IEEE Computer Society International Symposium on Information Science and Engieering, Shanghai, China, Dec , pp [3] G.Selvakumar and K.Boopathy Bagan, An Efficient QRS Complex Detection Algorithm Using Optimal Wavelet, Proc. Of The 6th WSEAS Int. Conf. On Signal Processing, Computational Geometry & Artificial Vision, Elounda, Greece, August 2-23, 26, Pp5-55. [3] S. Z. Mahmoodabadi, A. Ahmadian and M. D. Abolhasani, ECG Feature Extraction Using Daubechies Wavelets, Proceedings of the 5 th IASTEAD Int. Conf. on Visualization, Imaging and Image Processing, September 7-9, 25, Benidorm, Spain. [32] A Illanes-Manriquez and Q Zhang, An Algorithm for Robust Detection of QRS Onset and Offset in ECG Signals, Computers in Cardiology, vol. 35, pp , 28. [33] S. Benitez et al., A new QRS detection algorithm based on the Hilbert transform, Computers in Cardiology, vol. 27, pp , 2. [34] Chee-Hung Henry Chu and Edward J. Delp, Impulsive Noise Suppression And Background Normalization of Electrocardiogram Signals Using Morphological Operators, IEEE Transactions on Biomedical Engineering, 989;36(2): [35] Trahanias P.E., An Approach to QRS Complex Detection Using Mathematical Morphology, IEEE Transactions on Biomedical Engineering, 993; 4(2):2-24. [36] Yongli Chen and Huilong Duan, A QRS Complex Detection Algorithm Based on Mathematical Morphology and Envelope, Proceedings of the 27 th IEEE EMBC, Shanghai, China, September - 4, 25, pp [37] V.S. Chouhan and S.S. Mehta, Detection of QRS Complexes in 2- lead ECG using Adaptive Quantized Threshold, International Journal of Computer Science and Network Security, Vol.8 No., pp 55-63, January 28. [38] Mohamed Elgendi, Sivaram Mahalingam, Mirjam Jonkman and Friso De Boer, A Robust QRS Complex Detection Algorithm using Dynamic Thresholds, Proceedings of the International Symposium on Computer Science and its Applications, pp 53-58, 28. [39] Mohamed Elgendi, Mirjam Jonkman and Friso De Boer, Improved QRS Detection Algorithm using Dynamic Thresholds, International Journal of Hybrid Information Technology, Vol. 2, No., January 29.