Blood Glucose Detection Based on Waveform Features

As one of the necessary diabetes control and treatment methods, the photoacoustic blood glucose detection technology has great potential due to its deep detection depth and low interference from stray light. Previous research mainly focused on improving the detection capabilities of hardware systems and ignored the exploration of the physical meaning of the signal itself. We proposed a waveform feature to model the amplitude attenuation rate of the photoacoustic signal amplitude decay using the forced damping vibration equation. Moreover, facing low accuracy of blood glucose prediction in the case of small data, we proposed a stable and effective blood glucose detection combining time-frequency feature and waveform features with evidential regression. Finally, through a large of experiments, the minimum error is achieved $13.28 \pm 10.33 {\mathrm mg/dL}$, respectively.