Developing A Deep Learning Solution to Estimate Instantaneous Glucose Level from Heart Rate Variability

A IoT of studies address the use of IoT devices coupled with machine learning to predict and better detect health problems. Diabetes is an issue that society is struggling with for a very long time. The ease with which ECG signals can be recorded and interpreted provides an opportunity to use Deep Learning techniques to predict the estimated Sugar Levels of a patient. This research aims at describing a Deep Learning approach to provide models for different short-term heart rate variability measurements. Our approach is based on a special method to calculate Heart Rate Variability (HRV) with identification of segments, then averaging and concatenating them to exploit better feature engineering results. The short-term HRV is used for the determination of instantaneous plasma glucose levels. The Deep Learning method is based on Autokeras, the neural architectural search provided the best results for the 15-minute measurements. Our research question is to develop a solution to estimate the Instantaneous glucose value from heart rate variability with sufficient quality. The evaluated test set gave the following results: RMSE(0.368), MSE(0.193), R square(0.513), and R squared loss(0.541).