| dc.description.abstract | Sleep disorders significantly impact human health and well-being, highlighting the need
for accessible and efficient diagnostic tools. Traditionally, diagnosing these disorders
involved uncomfortable overnight stays in sleep labs, limiting long-term monitoring and
requiring expert oversight. Researchers have turned to wearable devices for automated
sleep classification systems to overcome these challenges. Among these technologies,
photoplethysmography (PPG) signals have emerged as a key component in developing
automatic frameworks for multi-stage sleep classification. In this study, both machine
learning (XGBoost) and deep learning techniques (LSTM, CNN) were employed to create
models for classifying two-stage (Wake-Sleep) and three-stage (Wake-NREM-REM) sleep
patterns. The proposed approach achieved an average classification accuracy of 87% and
76% for two and three stages, surpassing existing state-of-the-art models in the field. By
utilizing wearable devices equipped with PPG sensors, individuals can now monitor their
sleep patterns in real time, facilitating early detection and intervention for sleep disorders
and ultimately enhancing overall health outcomes. | en_US |
