Diabetes mellitus represents a prominent global health concern, requiring continuous monitoring of blood glucose levels to prevent severe complications. Current glucose monitoring systems, such as capillary blood testing and continuous glucose monitors, are invasive or minimally invasive, leading to discomfort and risk of infection. Developing a reliable non-invasive method for glucose measurement would therefore represent a major challenge and advancement, significantly improving the quality of life of diabetic patients. The aim of this thesis is thus to study two non-invasive approaches for glucose level detection. The first investigates blood glucose estimation based on photoplethysmografic (PPG) signals combined with neural networks. Publicly available datasets (VitalDB and MUST) were used, which contain PPG recordings, blood glucose values and patient informations.