The International Arab Journal of Information Technology (IAJIT)


Swarm Intelligence Approach to QRS Detection Mohamed Belkadi and Abdelhamid Daamouche

The QRS detection is a crucial step in ECG signal analysis; it has a great impact on the beats segmentation and in the final classification of the ECG signal. The Pan-Tompkins is one of the first and best-performing algorithms for QRS detection. It performs filtering for noise suppression, differentiation for slope dominance, and thresholding for decision making. All of the parameters of the Pan-Tompkins algorithm are selected empirically. However, we think that the Pan- Tompkins method can achieve better performance if the parameters were optimized. Therefore, we propose an adaptive algorithm that looks for the best set of parameters that improves the Pan-Tompkins algorithm performance. For this purpose, we formulate the parameter design as an optimization problem within a particle swarm optimization framework. Experiments conducted on the 24 hours recording of the MIT/BIH arrhythmia benchmark dataset achieved an overall accuracy of 99.83% which outperforms the state-of-the-art time-domain algorithms.

[1] Bal R. and Kumar A., “Improved QRS Detector Using Parallel based Hybrid Mamemi Filter,” International Journal of Image, Graphics and Signal Processing, vol. 9, no. 3, pp. 55-61, 2017.

[2] Benmalek M. and Charef A., “Digital Fractional- Order Operators for R-Wave Detection in TheElectrocardiogram,” IET Signal Process, vol. 3, no. 5, pp. 381-391, 2008.

[3] Castells-Rufas D. and Carrabina J., “Simple Real- Time QRS Detector with the Mamemi Filter,” Biomedical Signal Processing and Control, vol. 21, pp. 137-145, 2015.

[4] Chen S., Chen H., and Chan H., “A Real-Time QRS Detection Method Based on Moving- Averaging Incorporating with Wavelet Denoising,” Computer Methods and Programs in Biomedicine, vol. 82, no. 3, pp. 187-195, 2006.

[5] Elgendi M., “Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases,” PlosONE, vol. 8, no. 9, 2013.

[6] Gutiérrez-Rivas R., García J., Marnane W., and Hernández Á., “Novel Real-Time Low- Complexity QRS Complex Detector Based on Adaptive Thresholding,” IEEE Sensors Journal, vol. 15, no. 10, pp. 6036-6043, 2015.

[7] Hamilton P. and Tompkins W., “Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database,” IEEE Transactions on Biomedical Engineering, vol. BME-33, no. 12, pp. 1157-1165, 1986.

[8] Hammad M., Ibrahim M., and Hadhoud M., “A Novel Biometric Based on ECG Signals and Images for Human Authentification,” The International Arab Journal of Information Technology, vol. 13, no. 6A, pp. 959-964, 2016.

[9] Hashim M., Hau Y., and Baktheri R., “Efficient QRS Complex Detection Algorithm Implementation on A Soc-Based Embedded System,” Jurnal Teknologi, vol. 78, no. 7-5, pp. 49-58, 2016.

[10] Kadambe S., Murray R., and Bordeaux-Bartels G., “Wavelet transform-based QRS Complex Detector,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 7, pp. 838-848, 1999.

[11] Karimipour A. and Homaeinezhad M., “Real- Time Electrocardiogram P-QRS-T Detection- Delineation Algorithm Based on Quality- Supported Analysis of Characteristic Templates,” Computers in Biology and Medicine, vol. 52, pp. 153-165, 2014.

[12] Mourad K. and Fethi B., “Efficient Automatic Detection of QRS Complexes in ECG Signal Based on Reverse Biorthogonal Wavelet Decomposition and Nonlinear Filtering,” Measurement, vol. 94, pp. 663-670, 2016.

[13] Li C., Zheng C., and Tai C., “Detection of ECG Characteristic Points Using Wavelet Transforms,” IEEE Transactions on biomedical Engineering, vol. 42, no. 1, pp. 21-28, 1995.

[14] Manikandan M. and Soman K., “A Novel Method for Detecting R-Peaks in Electrocardiogram (ECG) Signal,” Biomedical Signal Processing and Control, vol. 7, no. 2, pp. 118-128, 2012.

[15] Nguyen T., Qin X., Dinh A., and Bui F., “Low Resource Complexity R-Peak Detection Based on Triangle Template Matching and Moving Average Filter,” Sensors (Basel), vol. 19, no. 18, 2019.

[16] Pan J. and Tompkins W., “A Real-Time QRS Detector Algorithm,” IEEE Transactions on Biomedical Engineering,vol. BME-32, no. 3, pp. 230-236, 1985.

[17] Poli R., Cagnoni S., and Valli G., “Genetic Design of Optimum Linear and Nonlinear QRS Detectors,” IEEE Transactions on Biomedical Engineering, vol. 42, no. 11, pp. 1137-1141, 1995. Swarm Intelligence Approach to QRS Detection 487

[18] Ravanshad N., Rezaee-Dehsorkh H., Lotfi R., and Lian Y., “A Level Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors,” IEEE Transactions on Biomedical Engineering, vol. 18, no. 1, pp. 183-192, 2013.

[19] Ruha A., Sallinen S., and Nissila S., “A Real- Time Microprocessor QRS Detector System with a 1-ms Timing Accuracy for the Measurement of Ambulatory HRV,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 3, pp. 159- 167, 1997.

[20] Saadi D., Tanev G., Flintrup M., Osmanagic A., Egstrup K., Hoppe K., Jennum J., Jeppesen J., Iversen H., and Sorensen H., “Automatic Real- Time Embedded QRS Complex Detection for a Novel Patch-Type Electrocardiogram Recorder,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1-12, 2015.

[21] Sedghamiz H., Complete Implementation of Pan- Tompkins Software. Humanist, Hooman Sedghamiz, 2014.

[22] Shaik B., Naganjaneyulu G., Chandrasheker T., and Narasimhadhan A., “A Method for QRS Delineation Based on STFT using Adaptive Threshold,” Procedia Computer Science, vol. 54, pp. 646-653, 2015.

[23] Suarez K., Silva J., Berthoumieu Y., Gomis P., and Najim M., “ECG Beat Detection Using A Geometrical Matching Approach,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 4, pp. 641-650, 2007.

[24] Tekeste T., Saleh H., Mohammad B., and Ismail M., “Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 2, pp. 669 -679, 2018.

[25] Uchaipichat N. and Sakonthawat I., “Development of QRS Detection using Short- Time Fourier Transform Based Technique,” International Journal of Computer Applications, pp. 7-10, 2010.

[26] Xiang Y., Lin Z., and Meng J., “Automatic QRS Complex Detection Using Two-Level Convolutional Neural Network,” Biomedical Engineering Online, vol. 17, no. 1, 2018.

[27] Xue Q., Hu Y., and Tompkins W., “Neural- Network-Based Adaptive Matched Filtering for QRS Detection,” IEEE Transactions on Biomedical Engineering, vol. 39, no. 4, pp. 317- 329, 1992.

[28] Yakut Ö. and Bolat E., “An Improved QRS Complex Detection Method Having Low Computational Load,” Biomedical Signal Processing and Control, vol. 42, pp. 230-241, 2018.

[29] Zhang F. and Lian Y., “QRS Detection Based on Multiscale Mathematical Morphology, for Wearable ECG Devices in Body Area Networks,” IEEE Transactions on Biomedical Circuits and Systems, vol. 3, no. 4, pp. 220-228, 2009.

[30] Zhang C. and BaeTae-Wuk., “VLSI Friendly ECG QRS Complex Detector for Body Sensor Networks,” EEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 1, pp. 52-59, 2012. Mohamed Belkadi born on 1989 at Algiers, Algeria, he obtained his B.E in Computer Science in 2012 and M.S degree in 2014 from Medea University. Currently, he is a Ph.D. student at the University of Boumerdes, Algeria. His research interests include Digital Signal Processing, Biomedical Signal Processing, and Deep Learning. Abdelhamid Daamouche received the state engineer degree in Telecommunications from (INELEC), Boumerdes, Algeria, and the Magister degree from the University of Batna, and a Ph.D. degree in signal and image processing from the University of Boumerdes, in 2012. In 2003, he joined the University of Boumerdes as a Lecturer, and in 2019 became a Professor. His current research interests include pattern recognition, biomedical engineering, and remote sensing.