Volume 10, Issue 1 (Jan-Mar 2021)                   JCHR 2021, 10(1): 52-59 | Back to browse issues page

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Akhoondan F, Hamidi H, Broumandnia A. Monitoring Patients to Prevent Myocardial Infarction using Internet of Things Technology. JCHR. 2021; 10 (1) :52-59
URL: http://jhr.ssu.ac.ir/article-1-681-en.html
1- 1. Faculty of computer engineering, Islamic Azad Univesity E-Campus, Tehran, Iran
2- 2. Faculty of industrial engineering, K.N.Toosi university of technology, Tehran, Iran , h_hamidi@kntu.ac.ir
3- 3. Faculty of computer engineering, Islamic Azad University South Tehran Branch, Tehran, Iran
Abstract:   (1259 Views)
Introduction: The Iranian Ministry of Health has announced that when a heart attack occurs, 50% of patients die within the first hours after a heart attack. The purpose of this article is to provide a system for 24-hour patient monitoring, prevention of heart attack and reduction of mortality.
Methods: In this original research study, by reviewing the valid articles of 2020, two sensor samples with the least error, fast and user-friendly were selected, then presented by new system methods including two-phase: warning transmission and normal mode. Received information from both of the phases is stored in the patient's digital file. Based on this information, personalized decisions can be made for each patient.
Results: According to the Iranian Ministry of Health and Medical Education, more than 40% of deaths in the country are related to the heart diseases, 19% of them are related to the heart attack, while 50% of deaths due to myocardial infraction happen in the first hours. Our proposed 24-hour monitoring system, using the most up-to-date and accurate measurement tools, reduces the risk by continuously measuring the patient's vital signs.
Conclusion: In our proposed system, the time and numerical interval of each measurement by the sensors are determined by the respective doctor, then the information is stored in each person's digital medical record. This system helps prescribe medication and make more accurate decisions based on the patient's specific circumstances. It is recommended that the drug delivery phase be performed within the arrival time of the medical team to minimize the risk.
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Review: Research | Subject: Health care management
Received: 2020/09/16 | Accepted: 2021/03/20 | Published: 2021/03/29

1. 1. Kang JJ, Adibi S. A Review of Security Protocols in mHealth Wireless Body Area Networks (WBAN). in International Conference on Future Network Systems and Security, Paris, France. 2015; 61-83. [DOI:10.1007/978-3-319-19210-9_5]
2. Gao W, Emaminejad S, Nyein HY, et al. Fully Integrated Wearable Sensor Arrays for Multiplexed in Situ Perspiration Analysis. Nature. 2016; 529(7587): 509-514. [DOI:10.1038/nature16521]
3. Kang JJ. An Inference System Framework for Personal Sensor Devices in Mobile Health and Internet of Things Networks . Submitted in Fulfilment of The Requirements for the PhD thesis, Deakin University January 2017.
4. Rosli RSB, Olanrewaju RF. Mobile Heart Rate Detection System (Moherds) for Early Warning of Potentiallyfatal Heart Diseases. in 2016 International Conference on Computer and Communication Engineering (ICCCE). 2016: 422-427.
5. Wolgast G, Ehrenborg C, Israelsson A, et al. Wireless body area network for heart attack detection . IEEE Antennas and Propagation Magazine. 2016; 58(5) : 84-92. [DOI:10.1109/MAP.2016.2594004]
6. Jambhulkar P, Baporikar V. Review on Prediction of Heart Disease Using Data Mining Technique with Wireless Sensor Network. International Journal of Computer Science and Applications. 2015; 8(1): 55-59.
7. Alansari Z, Soomro S, Belgaum MR, et al. The Rise of Internet of Things (IoT) in Big Healthcare Data: Review and Open Research Issues. Progress in Advanced Computing and Intelligent Engineering. 2018: 675-85. [DOI:10.1007/978-981-10-6875-1_66]
8. Fernandez-Carames MT, Fragra-Lamas P. Design of a Fog Computing, Blockchain and IoT-Based Continuous Glucose Monitoring System for Crowdsourcing mHealth. 5th International Electronic Conference on Sensors and Applications. 2018; 4(1): 37. [DOI:10.3390/ecsa-5-05757]
9. Martín A, Kim J, Kurniawan JF, et al. Epidermal Microfuidic Electrochemical Detection System: Enhanced Sweat Sampling and Metabolite Detection. ACS Sensors. 2017; 2(12): 1860-1868. [DOI:10.1021/acssensors.7b00729]
10. Huang X, Liu Y, Cheng H, et al. Materials and Designs for Wireless Epidermal Sensors of Hydration and Strain. Advanced Functional Materials. 2014; 24(25): 3846-3854. [DOI:10.1002/adfm.201303886]
11. Howsmon DP, Cameron F, Baysal N, et al. Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs). Sensors . 2017: 17(1); 161. [DOI:10.3390/s17010161]
12. Ding S, Schumacher M. Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review. Sensors . 2016; 16(4): 589. [DOI:10.3390/s16040589]
13. Haase K, Müller N, Petrich W, et al. Towards a continuous glucose monitoring system using tunable quantum cascade lasers. InBiomedical Vibrational Spectroscopy 2018: Advances in Research and Industry . 2018 ; 10490: 1049008. International Society for Optics and Photonics. [DOI:10.1117/12.2291745]
14. Isensee K, Müller N, Puccib A, et al. Towards a Quantum Cascade Laser-Based Implant for The Continuous Monitoring of Glucose. Analyst. 2018; 143(24): 6025- 36. [DOI:10.1039/C8AN01382A]
15. Rotenberg MY, Tian B. Bioelectronic Devices: Long-Lived Recordings. Nature Biomedical Engineering. 2017; 1(3): 1-2. [DOI:10.1038/s41551-017-0048]
16. Yokota T, Inoue Y, Terakawa Y, et al. Ultrafexible, Large-Area, Physiological Temperature Sensors for Multipoint Measurements. Proceedings of The National Academy of Sciences of the United States of America. 2015; 112(47): 14533-14538. [DOI:10.1073/pnas.1515650112]
17. Wang CH. Ultrasonic Device for Blood Pressure Measurement"; A Thesis Submitted in Partial Satisfaction of The Requirements. University of California San Diego. 2018.
18. Man S, Ter Haar CC, Maan AC, et al. The Dependence of The Stemi Classification on The Position of St-Deviation Measurement Instant Relative to The J Point. 2015 Computing in Cardiology Conference (CinC). 2015: 837 - 840. [DOI:10.1109/CIC.2015.7411041]
19. Wang  C, Li X, Hu H, et al. Monitoring of the Central Blood Pressure Waveform Via A Conformal Ultrasonic Device . Nature Biomedical Engineering. 2018; 2(9): 687-695. [DOI:10.1038/s41551-018-0287-x]
20. Dewan A, Sharma M. Prediction of Heart Disease Using A Hybrid Technique in Data Mining Classification. 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom). 2015:704-706.
21. Koshti M, Ganorkar S. Iot Based Health Monitoring System by Using Raspberrypi and Ecg Signal. International Journal of Innovative Research in Science, Engineering and Technology. 2016; 5(5): 8977- 85.
22. Medhekar DS, Bote MP, Deshmukh SD. Heart Disease Prediction System Using Naive Bayes. International Journal of Enhanced Research in Science Technology and Engineering. 2013; 2(3).
23. Raihan M, Mondal S, More A, et al. Smartphone Based Ischemic Heart Disease (Heart Attack) Risk Prediction Using Clinical Data and Data Mining Approaches, A Prototype Design. 2016 19th International Conference on Computer and Information Technology (ICCIT). 2016: 299 - 303. [DOI:10.1109/ICCITECHN.2016.7860213]

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