International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

A Portable IoT-cloud ECG Monitoring System for Healthcare

  • Qtaish, Amjad (College of Computer Science and Engineering, University of Ha'il) ;
  • Al-Shrouf, Anwar (Department of Biomedical Equipment Technology, Prince Sattam Bin Abdulaziz University)
  • Received : 2021.12.05
  • Published : 2022.01.30
Download PDF
⟨ Previous Next ⟩

Abstract

Public healthcare has recently become an issue of great importance due to the exponential growth in the human population, the increase in medical expenses, and the COVID-19 pandemic. Speed is one of the crucial factors in saving life, particularly in case of heart attack. Therefore, a healthcare device is needed to continuously monitor and follow up heart health conditions remotely without the need for the patient to attend a medical center. Therefore, this paper proposes a portable electrocardiogram (ECG) monitoring system to improve healthcare for heart attack patients in both home and ambulance settings. The proposed system receives the ECG signals of the patient and sends the ECG values to a MySQL database on the IoT-cloud via Wi-Fi. The signals are displayed as an ECG data chart on a webpage that can be accessed by the patient's doctor based on the HTTP protocol that is employed in the IoT-cloud. The proposed system detects the ECG data of the patient to calculate the total number of heartbeats, number of normal heartbeats, and the number of abnormal heartbeats, which can help the doctor to evaluate the health status of the patient and decide on a suitable medical intervention. This system therefore has the potential to save time and life, but also cost. This paper highlights the five main advantages of the proposed ECG monitoring system and makes some recommendations to develop the system further.

Keywords

References

  1. M. K. Abadi, R. Subramanian, S. M. Kia, P. Avesani, I. Patras, and N. Sebe, DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses, IEEE Trans. Affect. Comput., Vol. 6, No. 3, pp. 209-222, July-Sept., 2015. https://doi.org/10.1109/TAFFC.2015.2392932
  2. A. Mishra, B. Chakraborty, D. Das, and P. Bose, AD8232 based Smart Healthcare System using Internet of Things (IoT), Int. J. Eng. Res. Technol., Vol. 7, pp. 13-17, April, 2018. https://doi.org/10.15623/ijret.2018.0705004
  3. Z. Yang, Q. Zhou, L. Lei, K. Zheng, and W. Xiang, An IoT-cloud Based Wearable ECG Moni-toring System for Smart Healthcare, J. Med. Syst., Vol. 40, No. 12, pp. 1-18, October, 2016. https://doi.org/10.1007/s10916-015-0365-5
  4. M. Chhabra and M. Kalsi, Real Time ECG monitoring system based on Internet of Things (IoT), Int. J. Sci. Res. Publ., Vol. 7, No. 8, pp. 547-550, August, 2017.
  5. Cleveland Clinic. [Online]. Available: http://my.clevelandclinic.org/services/heart/disorders/arrhythmia/sudden-cardiac-death.
  6. M. Javadi, S. A. A. A. Arani, A. Sajedin, and R. Ebrahimpour, Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning, Biomed. Signal Process. Control, Vol. 8, No. 3, pp. 289-296, May, 2013. https://doi.org/10.1016/j.bspc.2012.10.005
  7. M. W. Wildan Gifari, H. Zakaria, and R. Mengko, Design of ECG Homecare: 12-lead ECG ac-quisition using single channel ECG device developed on AD8232 analog front end, 5th Interna-tional Conference on Electrical Engineering and Informatics, pp. 371-376, 2015.
  8. L. Hou et al., Internet of Things Cloud: Architecture and Implementation, IEEE Commun. Mag., Vol. 54, No. 12, pp. 32-39, December, 2016. https://doi.org/10.1109/MCOM.2016.1600398CM