Analysis of QRS Peak and Edge Detection in ECG Signal Using Entropy

Lokesh S.*, Udhayakumar G.**
* Department of Electronics and Communication Engineering, Vel Tech, Chennai, Tamil Nadu, India.
** Department of Electrical and Electronics Engineering, SRM Valliammai Engineering College, Kanchipuram, Tamil Nadu, India.
Periodicity:April - June'2019


Electrocardiogram (ECG) is the most vital and widely used methodology to check the cardiovascular diseases. For identifying arrhythmia classification, it needs large storage space and intensive manual effort. The conventional technique of visual analysis to examine the ECG signals by doctors or physicians is not effective and time consuming. In this work, an attempt has been made towards the development of an automated system for investigation of QRS wave in ECG signals using Entropy and Edge detection. The Peak detection process starts only when the enhanced signal exceeds the preset average threshold. Conventional ECG signals are used from MIT/BIH arrhythmia database for this study. The ECG signals are processed using edge based detection and related to Pan-Tompkins algorithm for extracting the QRS features using Entropy threshold of edge detection operators. The results show that the proposed system has a sensitivity of 99.2% and accuracy values of 98.11% and a positive prediction of 98.9% using Prewitt based edge detection.


ECG, QRS detection, Prewitt Operator, Entropy

How to Cite this Article?

Lokesh. S., & Udhayakumar, G . (2019). Analysis of QRS Peak and Edge Detection in ECG Signal using Entropy. i-manager’s Journal on Digital Signal Processing. 7(2), 10-14.


[1]. Amiri, A. M., & Mankodiya, K. (2015, October). M-QRS: An efficient QRS detection algorithm for mobile health applications. In 2015 17th International Conference on E- health Networking, Application and Ser vices (HealthCom) (pp. 673-676). IEEE. HealthCom.2015.7454590
[2]. Dua, N., & Kumra, V. P. (2007). Management of perioperative arrhythmias. Indian Journal of Anaesthesia, 51(4), 310-323.
[3]. Gao, J., Hu, J., & Tung, W. W. (2012). Entropy measures for biological signal analyses. Nonlinear Dynamics, 68(3), 431-444. 11071-011-0281-2
[4]. Hirai, Y., Matsuoka, T., Tani, S., Isami, S., Tatsumi, K., Ueda, M., & Kamata, T. (2019). A biomedical sensor system with stochastic A/D conversion and error correction by machine learning. IEEE Access, 7, 21990- 22001.
[5]. Jain, S., Ahirwal, M. K., Kumar, A., Bajaj, V., & Singh, G. K. (2017). QRS detection using adaptive filters: A comparative study. ISA Transactions, 66, 362-375.
[6]. Luz, E. J. D. S., Schwartz, W. R., Cámara-Chávez, G., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine, 127, 144-164.
[7]. MIT–BIH Arrhythmia Database Directory. (1992). Harvard-MIT Division of Health Sciences and Technology Biomedical Engineering Center. Retrieved from tdbdir/mitdbdir.htm
[8]. Mitra, M., & Samanta, R. K. (2013). Cardiac arrhythmia classification using neural networks with selected features. Procedia Technology, 10, 76-84.
[9]. Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomedical Signal Processing and Control, 47, 41-48. 2018.08.007
[10]. Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 32(3), 230-236. TBME.1985.325532
[11]. Rashmi, Kumar, M., & Saxena, R. (2013). Algorithm and technique on various edge detection: A survey. Signal and Image Processing, 4(3), 65-75. 10.5121/sipij.2013.4306
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