S.V. Frolov1, A.A. Korobov2, K.S. Savinova3, A.Yu. Potlov4, T.A. Frolova5

1–5 Tambov State Technical University (Tambov, Russia)
1 sergej.frolov@gmail.com, 2 korobov1991@mail.ru, 3 savinova.k94@mail.ru, 4 zerner@yandex.ru, 5 frolova2000@gmail.com

The problem of developing an effective control system for a neonatal incubator is considered. The author's experimental setup and neonatal phantoms are proposed for studying the features of stabilization of heat and humidity characteristics in an incubator for newborns.

The aim of the work is development of an experimental setup and creation of a full-scale hydrodynamic phantom of a newborn’s body to study the specific features of microclimate control in neonatal incubators.

The method of gradient neural network control of microclimate parameters was configured and evaluated in an environment as close as possible to real clinical usages, where the most suitable microclimate conditions are set individually for each patient by the attending neonatologist. The work was carried out using the above experimental setup to study a variety of options for stabilizing the given conditions of microclimate in neonatal incubators and a hydrodynamic phantom of a newborn.

The practical value of the research result lies in the development of a system structure and a method for more effective controlling the microclimate in neonatal incubator.

Frolov S.V., Korobov A.A., Savinova K.S., Potlov A.Yu., Frolova T.A. Experimental setup for studying the specific features of microclimate control in neonatal incubators using a hydrodynamic phantom of a newborn. Biomedical radioelectronics. 2024. T. 27. № 2.
P. 52–59. DOI: https://doi.org/10.18127/j15604136-202402-07 (in Russian)

1. Nearly 30 million sick and premature newborns in dire need of treatment every year. https://www.who.int/news/item/13-12-2018-nearly-30-million-sick-and-premature-newborns-in-dire-need-of-treatment-every-year (обновление 15.09.2023).
2. Campbell D. E. Neonatology for Primary Care. American Academy of Pediatrics. 2019. 2nd edition. 1100 P.
3. Hadj Ali J. El, Feki E., Mami A. Tuning PID using particle swarm optimization for controlling temperature of the infant incubator. International Journal of Computer Science and Network Security. 2020. V. 20, №3. P. 174–182.
4. Ben Ali R., Jaballah M., Aridhi E., Mami A. Design and FPGA-implementation of a PID controller for temperature control in a refrigeration system. Indian Journal of Science and Technology. 2018. V. 11. P. 1–14. DOI: 10.17485/ijst/2018/v11i16/121762
5. Irianto B.G., Maghfiroh A.M., Sofie M., Kholiq A., Musvika S.D., Akbar D.A. Controlling the Temperature of PID System-Based Baby Incubator to Reduction Overshoot. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds). Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering. 2023. V. 1008. DOI: 10.1007/978-981-99-0248-4_35
6. Paternain Soler C. Prototyping a closed loop control system for a neonatal incubator Projecte. Treball Final de Carrera, UPC, Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona, Departament d'Enginyeria Elèctrica. 2009.
7. Sumardi, Sadi; Darjat,; Sinuraya, Enda Wista & Pamungkas, Rahmat Jati. Design of Temperature Control System for Infant Incubator using Auto Tuning Fuzzy-PI Controller. International Journal of Engineering and Information Systems (IJEAIS). 2019. V. 3 (1). P. 1–7.
8. Utomo, Satryo & Irawan, Januar & Imron, Arizal & Nari, Mochamad Irwan & Amalia, Rosida. Automatic baby incubator system with fuzzy-PID controller. IOP Conference Series: Materials Science and Engineering. 2021. №1034. 012023. DOI: 10.1088/1757-899X/1034/1/012023
9. Reddy, Narender & Mathur, Garima & Hariharan S.I. Toward a Fuzzy Logic Control of the Infant Incubator. Annals of biomedical engineering. 2009. № 37. 2146-52. DOI: 10.1007/s10439-009-9754-6
10. Hadj Ali J. El, Feki E., Zermani M. A., de Prada C., Mami A. Incubator system identification of humidity and temperature: Comparison between two identification environments. 9th International Renewable Energy Congress (IREC), Hammamet, Tunisia. 2018. P. 1–6. DOI: 10.1109/IREC.2018.8362529
11. Feki E., Zermani M.A., Mami A. GPC Temperature Control of A Simulation Model Infant-Incubator and Practice with Arduino Board. International Journal of Advanced Computer Science and Applications. 2017. V. 8, № 6. P. 46-59. DOI: 10.14569/IJACSA.2017.080607
12. Feki E., Zermani M.A., Mami A. Decoupling Control Approach for Neonate Incubator System. International Journal of Computer Applications. 2012. V. 47, №2. P. 49–57. DOI: 10.5120/7164-9851.
13. Frolov S. V., Potlov A.Yu., Korobov A. A., Savinova K. S. Gradient Method for Neural Network Control of Multiconnected Nonlinear Nonstationary Stochastic Systems. Devices and systems. Management, control, diagnostics. 2021. №5. P. 41–48. DOI: 10.25791/pribor.5.2021.1262
14. Frolov S.V., Potlov A.Yu., Korobov A.A., Savinova K.S. Neural Network Control of Environmental Parameters in Neonatal Incubators. Proceedings of 2023 4th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2023. 2023. P. 21–24. DOI: 10.1109/NeuroNT58640.2023.10175837
15. Frolov S.V., Savinova K.S., Kulikov A.Yu., Sukonkin I.A. The use of effective positional control systems for medical equipment tasks. Models, systems, networks in economics, technology, nature and society. 2022. №2. P. 21–24. DOI: 10.1109/NeuroNT58640. 2023.10175837
16. Sarman, Ihsan, Depositor Robert L Bolin, Ingvar Holmér and Ragnar Tunell. Assessment of thermal conditions in neonatal care: use of a manikin of premature baby size. American journal of perinatology. 1992. V. 9 (4). P. 239–246. DOI: 10.1055/s-2007-994780
17. Frankenberger R.T., Bussmann O., Nahm W., Konecny E. Model for simulation of heat loss by premature infants. Biomed Tech (Berl). 1998. V. 43(5). P. 137–143. DOI:10.1515/bmte.1998.43.5.137
18. Lyra S., Voss F., Coenen A. et al. A neonatal phantom for vital signs simulation. IEEE Transactions on Biomedical Circuits and Systems. 2021. № 15 (5). P. 949–959. DOI: 10.1109/TBCAS.2021.3108066
19. Voss F., Lyra S., Blase D., Leonhardt S., Lüken M.A. Setup for Camera Based Detection of Simulated Pathological States Using a Neonatal Phantom. Sensors. 2022. № 22. P. 957. DOI: 10.3390/s22030957
20. Frolov S.V., Potlov A.Y., Frolova T.A., Proskurin S.G. Compression elastography and endoscopic optical coherence tomography for biomechanical properties evaluation of cerebral arteries walls with aneurysm and their phantoms. AIP Conference Proceedings. 2019. V. 2140, art. № 020020. DOI: 10.1063/1.5121945
21. Potlov A.Y., Frolov S.V., Proskurin S.G. Tissue-mimicking phantoms of human retina with co sideration to blood circulation for Doppler optical coherence tomography. Progress in Biomedical Optics and Imaging – Proceedings of SPIE. 2020. V. 11457, art. № 114571S. DOI: 10.1117/12.2563859