9th International Scientific Conference Technics and Informatics in Education – TIE 2022 (2022) стр. 148-152
АУТОР(И): Dejan Vujičić, Dijana Stojić, Đorđe Damnjanović, Dušan Marković, Siniša Ranđić
This paper presents the system for electrocardiogram measurements (ECG) using an Arduino microcontroller and AD8232 ECG sensor. The paper gives the basics of human heart anatomy and electrical activity which is enough for understanding the basic principles of ECG measurements. The hardware and software components are presented, as well as the given results. This system can be effectively used as an ECG measurement device and in biomedicine students’ education.
Arduino; ECG; human heart; measurements; sensor
-  Nemati, E., Deen, M. J., & Mondal, T. (2012). A wireless wearable ECG sensor for long-term applications. IEEE Communications Magazine, 50(1), 36-43.
-  Yamamoto, Y., Yamamoto, D., Takada, M., Naito, H., Arie, T., Akita, S., & Takei, K. (2017). Efficient skin temperature sensor and stable gel‐less sticky ECG sensor for a wearable flexible healthcare patch. Advanced healthcare materials, 6(17), 1700495.
-  Rashkovska, A., Depolli, M., Tomašić, I., Avbelj, V., & Trobec, R. (2020). Medical-grade ECG sensor for long-term monitoring. Sensors, 20(6), 1695.
-  Dey, N., Ashour, A. S., Shi, F., Fong, S. J., & Sherratt, R. S. (2017). Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Transactions on Consumer Electronics, 63(4), 442-449.
-  Park, Y. J., & Cho, H. S. (2013, October). Transmission of ECG data with the patch-type ECG sensor system using Bluetooth Low Energy. In 2013 International Conference on ICT Convergence (ICTC) (pp. 289-294). IEEE.
-  Chi, Y. M., & Cauwenberghs, G. (2010, June). Wireless non-contact EEG/ECG electrodes for body sensor networks. In 2010 International Conference on Body Sensor Networks (pp. 297-301). IEEE.
-  Jovanović, S., & Lotrić, N. (1987). Deskriptivna i topografska anatomija čoveka, Belgrade, Naučna knjiga, in Serbian
-  Basic anatomy of the human heart, available at: https://www.cardofmich.com/anatomy-human-heart-fun-facts/, accessed on July 14th 2022
-  Guyton, A., & Hall, J. (2006). Textbook of Medical Physiology. Elsevier Health Sciences TW.
-  Stanković, S. (2006). Fizika ljudskog organizma. Novi Sad, University of Novi Sad, Faculty of Science, in Serbian
-  Sick sinus syndrome, Mayo Clinic, available at: https://www.mayoclinic.org/diseases-conditions/sick-sinus-syndrome/symptoms-causes/syc-20377554, accessed on July 14th 2022
-  Licul, F. (1981). Elektrodijagnostika i elektroterapija, Zagreb, Školska knjiga, in Serbo-Croatian
-  The McGill Physiology Virtual Lab, available at: http://www.medicine.mcgill.ca/physio/vlab/ca rdio/introecg.htm, accessed on July 14th 2022
-  Pérez Riera AR., et al. (2008). „The enigmatic sixth wave of the electrocardiogram: the U wave“. Cardiol J. 15 (5), 408–21.
-  SparkFun Single Lead Heart Rate Monitor – AD8232, available at: https://www.sparkfun.com/products/12650, accessed on July 14th 2022
-  Analog Devices (2018). AD8232 Datasheet.
-  ECG Graph Monitoring with AD8232 ECG Sensor & Arduino, available at: https://how2electronics.com/ecg-monitoring-with-ad8232-ecg-sensor-arduino/, accessed on July 14th 2022
-  Kohler, B-U., Hennig, C., & Orglmeister, R. (2002). The Principles of Software QRS Detection. IEEE Engineering in Medicine and Biology. 21(1), 42-57.
-  Álvarez, R. A., Penín, A. J. M., & Sobrino, X. A. V. (2013). A comparison of three QRS detection algorithms over a public database. Procedia Technology, 9, 1159-1165.
-  Narayana, K. V. L., & Rao, A. B. (2011). Wavelet based QRS detection in ECG using MATLAB. Innovative Systems Design and Engineering, 2(7), 60-69.
-  Zidelmal, Z., Amirou, A., Adnane, M., & Belouchrani, A. (2012). QRS detection based on wavelet coefficients. Computer methods and programs in biomedicine, 107(3), 490- 496.
-  Pan, J., & Tompkins, W. (1985). A real-time QRS detection algorithm. IEEE transaction on biomedical engineering. 3, 230-235.
-  Chen, H. C., & Chen, S. W. (2003, September). A moving average based filtering system with its application to real-time QRS detection. In Computers in Cardiology, 2003 (pp. 585-588). IEEE.
-  Chen, S. W., Chen, H. C., & Chan, H. L. (2006). A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising. Computer methods and programs in biomedicine, 82(3), 187- 195.
-  Xue, Q., Hu, Y. H., & Tompkins, W. J. (1992). Neural-network-based adaptive matched filtering for QRS detection. IEEE Transactions on biomedical Engineering, 39(4), 317-329.
-  Abibullaev, B., & Seo, H. D. (2011). A new QRS detection method using wavelets and artificial neural networks. Journal of medical systems, 35(4), 683-691.
-  Šarlija, M., Jurišić, F., & Popović, S. (2017, September). A convolutional neural network based approach to QRS detection. In Proceedings of the 10th international symposium on image and signal processing and analysis (pp. 121-125). IEEE.