Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems
Type 1 diabetes <i>mellitus</i> is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients....
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/11/664 |
| _version_ | 1797469077473591296 |
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| author | João Lucas Correia Barbosa de Farias Wallace Moreira Bessa |
| author_facet | João Lucas Correia Barbosa de Farias Wallace Moreira Bessa |
| author_sort | João Lucas Correia Barbosa de Farias |
| collection | DOAJ |
| description | Type 1 diabetes <i>mellitus</i> is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes <i>mellitus</i>. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values. |
| first_indexed | 2024-03-09T19:16:20Z |
| format | Article |
| id | doaj.art-9b4d74e5e4ed416badaf2f51a824b85b |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T19:16:20Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-9b4d74e5e4ed416badaf2f51a824b85b2023-11-24T03:46:58ZengMDPI AGBioengineering2306-53542022-11-0191166410.3390/bioengineering9110664Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery SystemsJoão Lucas Correia Barbosa de Farias0Wallace Moreira Bessa1Department of Mechanical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, BrazilDepartment of Mechanical and Materials Engineering, University of Turku, 20500 Turku, FinlandType 1 diabetes <i>mellitus</i> is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes <i>mellitus</i>. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.https://www.mdpi.com/2306-5354/9/11/664artificial pancreasautomated insulin deliveryblood glucose regulationintelligent controlradial basis functionsneural networks |
| spellingShingle | João Lucas Correia Barbosa de Farias Wallace Moreira Bessa Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems Bioengineering artificial pancreas automated insulin delivery blood glucose regulation intelligent control radial basis functions neural networks |
| title | Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems |
| title_full | Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems |
| title_fullStr | Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems |
| title_full_unstemmed | Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems |
| title_short | Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems |
| title_sort | intelligent control with artificial neural networks for automated insulin delivery systems |
| topic | artificial pancreas automated insulin delivery blood glucose regulation intelligent control radial basis functions neural networks |
| url | https://www.mdpi.com/2306-5354/9/11/664 |
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