Non-Invasive Continuous Glucose Monitoring using Near Infrared Sensors
and PSO-ANN Algorithm
Abstract
Blood glucose is typically measured using invasive methods such as
finger pricking, which although accurate are not suitable for frequent
use as they cause extreme pain, and do not provide provisions for
continuous glucose monitoring. Recent studies have proposed non-invasive
glucometers that are based on scientific principles such as optical
polarimetry, thermal emission, and electromagnetic approaches, but are
expensive, highly sensitive to external noise and environmental
variations, have low signal-to-noise ratio (SNR), and poor glucose
selectivity. Although developments in Near-Infrared Spectroscopy (NIRS)
have overcome these limitations to a certain extent, they do not produce
reliable measurements due to large calibration errors that often result
in incorrect glucose readings. In this paper, we propose a robust
particle-swarm optimization-based artificial neural network for
non-invasive continuous glucose monitoring using the principles of NIRS.
We show that the PSO-ANN approach outperforms the traditional
backpropagation algorithm used in ANN training and several other
regression algorithms with the lowest error metrics: MAE- 1.01,
MSE-2.16, RMSE-0.97, R-sqaured score -0.976 and modified R-squared score
-0.973. The paper also provides insights into the circuit design,
sensors used, hardware-software integration, and clinical validation
alongside providing an overview of HbA1c computation.The accuracy and
reliability of the proposed system are analyzed using the Clarke Error
Grid (CEG) with 93.9% of the obtained readings falling within zone A
and 100% of the readings falling in the clinically accepted range
(zones A and B). The paper also explores potential enhancements such as
miniaturization of the prototype device for wearable applications and
wireless connectivity.Â