مشروع البحث:
Sleep Apnoea Detection with Smart Internet of Things Technology

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المساهمين
الممولين
رقم التعريف
696
الباحث
Ragab Ambark Seedi Ali Barika
المشرفين
منشورات
وحدات تنظيمية
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وحدة تنظيمية
الوصف
The 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.
الكلمات الدالة
Technology