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Noninvasive blood glucose level monitoring for predicting insulin infusion rate using multivariate data

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Diabetes stands as the most widely recognized acute disease globally, resulting in death when it is not treated in an appropriate manner and time. We have developed a closedloop control system that uses continuous glucose, carbohydrate, and physiological variable data to regulate glucose levels and treat hyperglycemia and hypoglycemia, as well as a hypoglycemia early warning module. Overall, the proposed models are effective at predicting a normal glycemic range from >70 to 180 mg/dl, hypoglycemic values of <70 mg/dl, and hyperglycemic value of 180 mg/dl blood sugar levels. We undertook a seven-day, day-and-night home study with 15 adults. Initially, we started with checking insulin levels after meal consumption, and later, we concentrated on how our system reacted to the physical activity of the patients. Evaluation was conducted based on performance parameters such as precision (0.87), recall (0.87), F-score (0.82), delay (26.5±3), and error size (1.14±2).
Rocznik
Strony
157--174
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
  • Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
autor
  • Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
Bibliografia
  • 1. A. Ramachandran et al., mDiabetes initiative using text messages to improve lifestyle and health-seeking behaviour in India, BMJ Innovations, 4(3): 155–162, 2018.
  • 2. M.G.R. Alam, S.F. Abedin, S.I. Moon, A. Talukder, C.S. Hong, Healthcare IoT-based affective state mining using a deep convolutional neural network, IEEE Access, 7: 75189–75202, 2019, doi: 10.1109/ACCESS.2019.2919995.
  • 3. H. Yin, N.K. Jha, A health decision support system for disease diagnosis based on wearable medical sensors and machine learning ensembles, IEEE Transactions on Multi-Scale Computing Systems, 3(4): 228–241, 2017, doi: 10.1109/TMSCS.2017.2710194.
  • 4. M. Yaghoubi, K. Ahmed, Y. Miao, Wireless Body Area Network (WBAN): A survey on architecture, technologies, energy consumption, and security challenges, Journal of Sensor and Actuator Networks, 11(4): 67, 2022, doi: 10.3390/jsan11040067.
  • 5. A. Jamal et al., Blood glucose monitoring and sharing amongst people with diabetes and their facilitators: Cross-sectional study of methods and practices, JMIR Diabetes, 6(4): e29178, 2021, doi: 10.2196/29178.
  • 6. J.L. Olson et al., The design of an evaluation framework for diabetes self-management education and support programs delivered nationally, BMC Health Services Research, 22: 46, 2022, doi: 10.1186/s12913-021-07374-4.
  • 7. M. Zhao, K. Hoti, H.Wang, D. Katabi, Assessment of medication self-administration using artificial intelligence, Nature Medicine, 27: 727–735, 2021, doi: 10.1038/s41591-021-01273-1.
  • 8. S. Alian, J. Li, V. Pandey, A personalized recommendation system to support diabetes self-management for American Indians, IEEE Access, 6: 73041–73051, 2018, doi: 10.1109/ACCESS.2018.2882138.
  • 9. W. Tang, J. Ren, K. Deng, Y. Zhang, Secure data aggregation of lightweight e-healthcare IoT devices with fair incentives, IEEE Internet of Things Journal, 6(5): 8714–8726, 2019, doi: 10.1109/JIOT.2019.2923261.
  • 10. L. Tang, S.J. Chang, C.-J. Chen, J.-T. Liu, Non-invasive blood glucose monitoring technology: A review, Sensors, 20(23): 6925, 2020, doi: 10.3390/s20236925.
  • 11. R. Ajjan, D. Slattery, E. Wright, Continuous glucose monitoring: A brief review for primary care practitioners, Advances in Therapy, 36(3): 579–596, 2019, doi: 10.1007/s12325-019-0870-x.
  • 12. J.D. Adkison, P.E. Chung, Implementing continuous glucose monitoring in clinical practice, Family Practice Management, 28(2): 7–14, 2021.
  • 13. Z. Zhang, H. Hu, X. Hu, Routing protocol for healthcare applications data over the 6LoWPAN-based wireless sensor networks, Procedia Computer Science, 225: 2153–2162, 2023, doi: 10.1016/j.procs.2023.10.206.
  • 14. H.H. Alshammari, The internet of things healthcare monitoring system based on MQTT protocol, Alexandria Engineering Journal, 69: 275–287, 2023, doi: 10.1016/j.aej.2023.01.065.
  • 15. R.A. Rahman, N.S.A. Aziz, M. Kassim, M.I. Yusof, IoT-based personal health care monitoring device for diabetic patients, [in:] 2017 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Langkawi, Malaysia, pp. 168–173, 2017, doi: 10.1109/ISCAIE.2017.8074971.
  • 16. D. Sierra-Sosa et al., Scalable healthcare assessment for diabetic patients using deep learning on multiple GPUs, IEEE Transactions on Industrial Informatics, 15(10): 5682–5689, 2019, doi: 10.1109/TII.2019.2919168.
  • 17. M.A. Al-Taee, W. Al-Nuaimy, Z.J. Muhsin, A. Al-Ataby, Robot assistant in management of diabetes in children based on the internet of things, IEEE Internet of Things Journal, 4(2): 437–445, 2017, doi: 10.1109/JIOT.2016.2623767.
  • 18. S.F. Ismail, IOE solution for a diabetic patient monitoring, [in:] 2017 8th International Conference on Information Technology (ICIT), Amman, Jordan, pp. 244–248, 2017, doi: 10.1109/ICITECH.2017.8080007.
  • 19. L. Chen et al., OMDP: An ontology-based model for diagnosis and treatment of diabetes patients in remote healthcare systems, International Journal of Distributed Sensor Networks, 15(5), 2019, doi: 10.1177/1550147719847112.
  • 20. H. Agrawal, P. Jain, A.M. Joshi, Machine learning models for non-invasive glucose measurement: Towards diabetes management in smart healthcare, Health and Technology, 12: 955–970, 2022, doi: 10.1007/s12553-022-00690-7.
  • 21. A. Hina, W. Saadeh, Noninvasive blood glucose monitoring systems using near-infrared technology – A review, Sensors, 22(13): 4855, 2022, doi: 10.3390/s22134855.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-628e9020-5a30-4dd6-8fe7-14ada5900877
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