In–human testing of a non-invasive continuous low–energy microwave glucose sensor with advanced machine learning capabilities

• Published in: Biosensors & bioelectronics Vol. 241; p. 115668
• Main Authors: Kazemi, Nazli, Abdolrazzaghi, Mohammad, Light, Peter E., Musilek, Petr
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2023
• Subjects: Anomaly Detection; Glucose; LSTM; Machine Learning; Microwave Sensor; Time Series Neural Network; Anomaly Detection; Microwave Sensor; LSTM; Glucose; Time Series Neural Network; Machine Learning
• ISSN: 0956-5663; 1873-4235
• DOI: 10.1016/j.bios.2023.115668

Continuous glucose monitoring schemes that avoid finger pricking are of utmost importance to enhance the comfort and lifestyle of diabetic patients. To this aim, we propose a microwave planar sensing platform as a potent sensing technology that extends its applications to biomedical analytes. In this paper, a compact planar resonator-based sensor is introduced for noncontact sensing of glucose. Furthermore, in vivo and in-vitro tests using a microfluidic channel system and in clinical trial settings demonstrate its reliable operation. The proposed sensor offers real-time response and a high linear correlation (R2 ∼ 0.913) between the measured sensor response and the blood glucose level (GL). The sensor is also enhanced with machine learning to predict the variation of body glucose levels for non-diabetic and diabetic patients. This addition is instrumental in triggering preemptive measures in cases of unusual glucose level trends. In addition, it allows for the detection of common artifacts of the sensor as anomalies so that they can be removed from the measured data. The proposed system is designed to noninvasively monitor interstitial glucose levels in humans, introducing the opportunity to create a customized wearable apparatus with the ability to learn.
•A reflection-based resonator is comprised of two coupled resonators to enhance sensitivity.•Glucose level in Interstitial fluid is linearly measured within a microfluidic channel.•Participants are asked for time-based glucose monitoring with a periodic chocolate intake.•The sensor response is highly correlated with the glucose level measured by a commercial device.•System anomalies are recognized by machine learning algorithm and removed.