Sleep plays a fundamental role in physical and mental health, and poor-quality sleep is associated with a wide range of chronic conditions. However, sleep disorders remain underdiagnosed and undertreated in many cases, in part due to the limited accessibility of accurate sleep monitoring. Although Polysomnography (PSG) is the gold standard for sleep monitoring, it is limited to controlled clinical settings, requires trained professionals, and is typically restricted to a single night of sleep that may not reflect the typical sleep behavior of the patient. Recent advances in wearable technology offer new opportunities for continuous, unobtrusive, and home-based sleep monitoring. This thesis explores a deep learning-based approach to automatic sleep staging using physiological signals collected from wearable devices to provide accessible tools for sleep assessment, particularly for individuals with suspected or diagnosed sleep disorders.