Dr. Ningrong Lei2025-08-262025-08-26https://dspace.academy.edu.ly/handle/123456789/1732The reference standard for diagnosing SA is polysomnography (PSG), conducted in a laboratory setting by trained professionals. However, this process is time-consuming, susceptible to human error, and demands technical expertise for both execution and interpretation. The inconvenience of in-lab PSG has spurred the need for new, simplified methods. This thesis posits that Computer-Aided Diagnosis (CAD) systems can enhance diagnostic efficacy. To explore this hypothesis, the thesis introduces innovative real-time detection techniques for Obstructive Sleep Apnoea (OSA) and the development of a high-performance OSA detection system. This system, offering continuous OSA detection, addresses the practical challenges associated with traditional diagnostic approaches. The integration of Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies, with a focus on the Lifetouch sensor, represents a novel approach to improve the accuracy of OSA detection. This innovative strategy aims to overcome barriers to timely and reliable diagnosis and monitoring of sleep disorders.Sleep apnoea (SA) is a hazardous condition characterized by interrupted breathing during sleep. This prevalent medical issue affects individuals of all ages, potentially leading to severe complications when untreated including, cardiovascular problems, diabetes, and daytime fatigue etc. Unfortunately, SA often remains undiagnosed due to the costly and inconvenient diagnostic procedures associated with it. It stands as a significant global health concern, impacting nearly one billion people worldwide, with a prevalence of 17 to 23% in women and 34 to 50% in men. SA is recognized as a risk factor for cardiovascular disorders (CVD) and carries substantial individual, societal, and economic burdens. The economic costs of SA diagnosis and treatment services run into billions of dollars annually.Technology