A Single LED Photoplethysmography-Based Noninvasive Glucose Monitoring Prototype System

Continuous glucose monitoring is essential for patients to avoid complications of both hypoglycemia and hyperglycemia. This paper presents a novel non-invasive continuous blood glucose monitoring system based on a single wavelength near-infrared (NIR) spectroscopy. The analog frontend of the system is designed with a single NIR LED to record the Photoplethysmographic (PPG) signal from the fingertip with motion artifacts removal and a bias current rejection up to 20uA. The proposed digital backend extracts 10 discriminating features from the PPG signal to predict the blood glucose level using (Exponential Gaussian Process) machine learning regression. To realize the feature extraction on FPGA, a novel two-dimensional structure of 256-point Fast Fourier Transform (FFT) is implemented which achieves a 47% reduction in complex multiplications compared to the conventional Radix-2 algorithm. The performance of the proposed system is validated using 200 patients PPG recordings and glucose levels measured via a commercial glucometer. It successfully predicts the glucose level with a mean absolute relative difference (mARD) of 8.97%.