Wireless ECG Monitor Design Based on Raspberry Pi3 and AD8232 Microchip

Abdulfattah Noorwali; Ameni Yengui; Ahmed Alzurqui; Kaissar Ammous; Anis Ammous

doi:10.15866/iree.v17i1.21152

Wireless ECG Monitor Design Based on Raspberry Pi3 and AD8232 Microchip

Abdulfattah Noorwali⁽¹⁾, Ameni Yengui^(2*), Ahmed Alzurqui⁽³⁾, Kaissar Ammous⁽⁴⁾, Anis Ammous⁽⁵⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iree.v17i1.21152

Abstract

This paper describes the designing and the development of a wireless electrocardiogram (ECG) monitoring system. In general, health monitoring is an attractive research area, and the proposed systems in the literature are expanding exponentially. Although it is hard to choose adequate technologies and designs that satisfy the needs and fulfil the requirements, an efficient and practical low-cost system has been built. In this work, a remote study process of the recorded electrical activity of the heart using a WiFi Module is achieved. The presented model considers transmitting the detected signals in real-time via wireless communication networks and then analyzing the results on Raspberry Pi 3. The received data will be stored, and digital filtering will be performed. While storing the data on files, the Raspberry Pi3 runs the Plotly streaming program simultaneously, which reads the data received with 10-bit precision and streams it in real-time for QRS detection processing. Then, detected and analyzed data will be exhibited on a website aligned with daily health advice based on patient health states. The webpage has been generated with Python based on the bokeh library, and it has been streamed using the Apache http web server. A portable ECG monitor system that complies with medical equipment design and electrical safety has been realized in this work.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords

Internet of Things; Wireless Sensor; Wireless Communication; ECG; Raspberry Pi3

Full Text:

PDF

References

World Health Organization et al., Health topics: Cardiovascular diseases, (last checked July 2012). Available: http://www.who. Int/topics/cardiovascular_diseases/en/

M.K. Islam, A.N.M.M. Haque, G. Tangim, T Ahammad and M.R.H. Khondokar, Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools, International Journal of Computer and Electrical Engineering, Vol. 4, no. 3, June 2012.
https://doi.org/10.7763/IJCEE.2012.V4.522

P. Sethi and S. R. Sarangi, Internet of things: architectures, protocols and applications, Journal of Electrical and Computer Engineering, 2017.
https://doi.org/10.1155/2017/9324035

M. Shelar, J. Singh and M Tiwari, Wireless patient health monitoring system, International Journal of Computer Applications, vol. 62, no.6, 2013.
https://doi.org/10.5120/10081-4697

L.V Rajani Kumari, Y. Padma Sai, and N. Balaji. Arduino based abnormal heart rate detection and wireless communication, International Journal on Cybernetics & Informatics (IJCI), vol.5, no. 4, 2016.
https://doi.org/10.5121/ijci.2016.5406

M. W. Alam, T. Sultana and M. S. Alam, A heartbeat and temperature measuring system for remote health monitoring using wireless body area network, International Journal of Bio-science and Biotechnology, vol. 8, no. 1, pp. 171-190, 2016.
https://doi.org/10.14257/ijbsbt.2016.8.1.16

V. P. Rachim, S. C. Kang, W.T. Chung and T.H. Kwon, Implementation of extended Kalman filter for real-time non contact ecg signal acquisition in android-based mobile monitoring system, Journal Sens. Sci. Technol, vol. 23, no. 1, pp. 7-14, 2014.
https://doi.org/10.5369/JSST.2014.23.1.7

V. Wahane and P.V. Ingole, An android based wireless ecg monitoring system for cardiac arrhythmia, IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT), pp. 183-187, 2016.
https://doi.org/10.1109/HIC.2016.7797727

N. Dey, A. S. Ashour, F. Shi, S. J. Fong and R. S. Sherrat, Developing residential wireless sensor networks for ecg healthcare monitoring, IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 442-449, 2017.
https://doi.org/10.1109/TCE.2017.015063

S. Majumder, L. chen, O. Marinov, C.H. Chen, T. Mondal and M J. Deen, Non contact wearable wireless ecg systems for long-term monitoring, IEEE Reviews in Biomedical Engineering, vol. 11, pp. 306-321, 2018.
https://doi.org/10.1109/RBME.2018.2840336

I. Zagan, V. Gheorghita Gaitan, A. I. Petrariu, N. Luga and A. Brezulianu, "Design, fabrication and testing of an iot healthcare cardiac monitoring device", Computers, vol. 9, no. 15, 2020.
https://doi.org/10.3390/computers9010015

D. A. Hasan and A. G. Ismaeel, Designing ECG Monitoring Healthcare System Based on Internet of Things Blynk Application, Journal of Applied Science and Technology Trends, vol. 01, no. 03, pp. 106 -111, 2020.
https://doi.org/10.38094/jastt1336

J. Jung , S. Shin, M. Kang, K. H. Kang and Y. T. Kim, Development of Wearable Wireless Electrocardiogram Detection System using Bluetooth Low Energy, Electronics, vol. 10, pp. 608, 2021.
https://doi.org/10.3390/electronics10050608

H. Li, G. Sun, Y. Li and R. Yang, Wearable Wireless Physiological Monitoring System Based on Multi-Sensor, Electronics, vol. 10, pp. 986, 2021.
https://doi.org/10.3390/electronics10090986

A.KM Jahangir A. Majumder, Y. A. ElSaadany, R. Young Jr. and DR. Ucci, An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest, Advances in Human-Computer Interaction, 2019.
https://doi.org/10.1155/2019/1507465

H. M. Abdul-Jabbar and J. K. Abed, Real Time Pacemaker Patient Monitoring System Based on Internet of Things IOP, Conf. Series: Materials Science and Engineering, 2020.
https://doi.org/10.1088/1757-899X/745/1/012093

I. Zagan, V. G. Gaitan, A. I. Petrariu and A. Brezulianu, Healthcare IoT m-GreenCARDIO Remote Cardiac Monitoring System - Concept, Theory of Operation and Implementation, Advances in Electrical and Computer Engineering, Vol. 17, no. 2, 2017.
https://doi.org/10.4316/AECE.2017.02004

H. Li and P. Boulanger, A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG), Sensors, vol. 20, 1461, 2020.
https://doi.org/10.3390/s20051461

J. Moeyersons, E. Smets, J. Morales, A. Villa, W. De Raedt, D. Testelmans, B. Buyse, C. V. Hoof, R. Willems, S. V. Huffel, C. Varon, Artefact detection and quality assessment of ambulatory ECG signals, Computer Methods and Programs in Biomedicine, vol. 182, 2019.
https://doi.org/10.1016/j.cmpb.2019.105050

M. Bravo-Zanoguera, D. Cuevas-González, J. P. García- Vázquez, R. L. Avitia and M.A. Reyna, Portable ecg system design using the ad8232 microchip and open-source platform, Multidisciplinary Digital Publishing Institute Proceedings, vol. 42, pp. 49, 2019.
https://doi.org/10.3390/ecsa-6-06584

M. A. Serhani, H. T. Elkassabi, H. Ismail and A. N. Navaz, ECG monitoring systems: Review, architecture, processes and key challenges, Sensors, vol. 20, no. 6, pp. 1796, 2020.
https://doi.org/10.3390/s20061796

M. Chhabra and M. Kalsi, Real time ecg monitoring system based on internet of things (iot), International Journal of Scientific and Research Publications, Vol. 7, no. 8, pp. 547-50, 2017.

J. A. Rincon, S. Guerra-Ojeda, C. Carrascosa and V. Julian, An IoT and Fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using Deep Learning Networks, Sensors, Vol. 20, no. 24, 2020.
https://doi.org/10.3390/s20247353

S. L. Pingale, Using pan Tompkins method, ECG signal processing and diagnose various diseases in MATLAB, In Proceedings of IRF International Conference, pp. 22-26, 2014.

S. Singh, Pattern analysis of different ECG signal using pan-tompkin's algorithm, (IJCSE) International Journal on Computer Science and Engineering, Vol. 2, Issue 7, 2502-2505, 2010.

Matlab Implementation of Pan Tompkins ECG QRS detector, March 2014.

https://www.researchgate.net/publication/313673153_Matlab_Implementation_of_Pan_Tompkins_ECG_QRS_detector

https://www.alibaba.com/product-detail/hospital-portable-mobile-wireless-bluetooth-electrocardiograph_62082610340.html?spm=a2700.wholesale.maylikeexp.6.6d402b7eqbIJ2M

Alkurawy, L., Saleh, M., Fatah, I., Saleh, A., Modeling and Identification of Human Heart System, (2019) International Journal on Engineering Applications (IREA), 7 (4), pp. 130-136.
https://doi.org/10.15866/irea.v7i4.17807

AlZoubi, O., AlAbabneh, N., Hmeidi, I., Bani Yassein, M., A Deep Learning System for the Diagnosis of Heart Problems from ECG Media Files, (2021) International Journal on Communications Antenna and Propagation (IRECAP), 11 (5), pp. 363-371.
https://doi.org/10.15866/irecap.v11i5.21132

Refbacks

There are currently no refbacks.

Username
Password
Remember me