Blood Pressure Monitoring from Photoplethysmography Based for Diabetes Detection

Abstract

Electrophysiological signals such as photoplethysmography (PPG) are useful for collecting human vital signs in clinical practice, especially when used wearable sensors like finger pulse oximeters for continuous diabetes monitoring. The PPG has been evaluated across subjects with varying anthropometrical characteristics to address challenges related to morphological properties. This study employed PPG-based methods for assessing diabetes by analyzing the relationship between the existence of blood glucose levels and blood pressure fluctuations. In this context, PPG-BP datasets were utilized to extract important features associated with pathological diabetes. As a result, PPG spectrogram images were utilized as inputs for a pre-trained deep learning model to effectively learn intrinsic signal patterns between healthy and diabetic individuals. The results suggested that the 2-D InceptionResNetV2 model was the most effective network for the classification problem, with an average accuracy of 95.95% using PPG data.