Detection of fasting blood sugar using a microwave sensor and convolutional neural network
Abstract Monitoring of fasting blood sugar (FBS) is a critical component in the diagnosis and management of diabetes, one of the most widespread chronic diseases globally. Microwave sensing—particularly through microstrip-based sensors—has recently gained attention as a promising technique for blood...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-06502-y |
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| author | Mohammad Amir Sattari Mohsen Hayati |
| author_facet | Mohammad Amir Sattari Mohsen Hayati |
| author_sort | Mohammad Amir Sattari |
| collection | DOAJ |
| description | Abstract Monitoring of fasting blood sugar (FBS) is a critical component in the diagnosis and management of diabetes, one of the most widespread chronic diseases globally. Microwave sensing—particularly through microstrip-based sensors—has recently gained attention as a promising technique for blood glucose monitoring, offering advantages such as low cost, high sensitivity, real-time response capability, and suitability for compact and wearable systems. In this study, a miniaturized microstrip microwave sensor is presented for non-contact FBS detection. Blood samples were directly collected from 78 individuals and analyzed using a clinical-grade auto-chemistry analyzer to determine reference FBS levels. Each sample was measured five times on the microwave sensor, resulting in a total of 390 transmission responses (S21) across a frequency range of 30 kHz to 18 GHz. These responses were recorded under controlled laboratory conditions, ensuring consistency and minimizing environmental interference. To interpret the complex, non-linear features of the sensor response, a convolutional neural network (CNN) was developed and trained using the entire dataset. The network demonstrated highly promising performance in estimating FBS values, achieving a mean relative error (MRE) of 1.31%. The results confirm the feasibility of combining broadband microwave sensing with deep learning techniques to enable reliable non-contact blood glucose measurement. This approach holds strong potential for integration into future wearable health monitoring systems, providing more user-friendly diabetic management tools without the frequent use of conventional blood sampling techniques. |
| format | Article |
| id | doaj-art-00875c442807442281b51a27a062db8d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-00875c442807442281b51a27a062db8d2025-08-20T03:03:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-06502-yDetection of fasting blood sugar using a microwave sensor and convolutional neural networkMohammad Amir Sattari0Mohsen Hayati1Electrical Engineering Department, Faculty of Engineering, Razi UniversityElectrical Engineering Department, Faculty of Engineering, Razi UniversityAbstract Monitoring of fasting blood sugar (FBS) is a critical component in the diagnosis and management of diabetes, one of the most widespread chronic diseases globally. Microwave sensing—particularly through microstrip-based sensors—has recently gained attention as a promising technique for blood glucose monitoring, offering advantages such as low cost, high sensitivity, real-time response capability, and suitability for compact and wearable systems. In this study, a miniaturized microstrip microwave sensor is presented for non-contact FBS detection. Blood samples were directly collected from 78 individuals and analyzed using a clinical-grade auto-chemistry analyzer to determine reference FBS levels. Each sample was measured five times on the microwave sensor, resulting in a total of 390 transmission responses (S21) across a frequency range of 30 kHz to 18 GHz. These responses were recorded under controlled laboratory conditions, ensuring consistency and minimizing environmental interference. To interpret the complex, non-linear features of the sensor response, a convolutional neural network (CNN) was developed and trained using the entire dataset. The network demonstrated highly promising performance in estimating FBS values, achieving a mean relative error (MRE) of 1.31%. The results confirm the feasibility of combining broadband microwave sensing with deep learning techniques to enable reliable non-contact blood glucose measurement. This approach holds strong potential for integration into future wearable health monitoring systems, providing more user-friendly diabetic management tools without the frequent use of conventional blood sampling techniques.https://doi.org/10.1038/s41598-025-06502-yMicrowave sensorMicrostripFasting blood sugarConvolutional neural networkDeep learningBiomedical sensing |
| spellingShingle | Mohammad Amir Sattari Mohsen Hayati Detection of fasting blood sugar using a microwave sensor and convolutional neural network Scientific Reports Microwave sensor Microstrip Fasting blood sugar Convolutional neural network Deep learning Biomedical sensing |
| title | Detection of fasting blood sugar using a microwave sensor and convolutional neural network |
| title_full | Detection of fasting blood sugar using a microwave sensor and convolutional neural network |
| title_fullStr | Detection of fasting blood sugar using a microwave sensor and convolutional neural network |
| title_full_unstemmed | Detection of fasting blood sugar using a microwave sensor and convolutional neural network |
| title_short | Detection of fasting blood sugar using a microwave sensor and convolutional neural network |
| title_sort | detection of fasting blood sugar using a microwave sensor and convolutional neural network |
| topic | Microwave sensor Microstrip Fasting blood sugar Convolutional neural network Deep learning Biomedical sensing |
| url | https://doi.org/10.1038/s41598-025-06502-y |
| work_keys_str_mv | AT mohammadamirsattari detectionoffastingbloodsugarusingamicrowavesensorandconvolutionalneuralnetwork AT mohsenhayati detectionoffastingbloodsugarusingamicrowavesensorandconvolutionalneuralnetwork |