Joint empirical mode decomposition, exponential function estimation and L1 norm approach for estimating mean value of photoplethysmogram and blood glucose level

Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical-based methods for performing the non-invasive blood glucose estimation for the consumer applications becomes very important. The authors' previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non-invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L-1 norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state-of-the-art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.