A Systematic Literature on Recent Advancement in Deep Learning to Diagnose Obstructive Sleep Apnea

• Published in: International Conference on Advanced Computing and Communication Systems (Online) Vol. 1; pp. 199 - 205
• Main Authors: Bano, Farhana, Abdul, Arif Mohammad
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.03.2024
• Subjects: Accuracy; Adaptation models; Classifier training; Deep learning; Diagnosis; Feature extraction; Intelligent detection mechanisms; Medical diagnostic imaging; Medical understanding; Polysomnography; Prediction algorithms; Signal processing algorithms; Sleep apnea; Technological advancement; Training; Transfer learning
• ISBN: 9798350384352
• ISSN: 2469-5556
• DOI: 10.1109/ICACCS60874.2024.10717093

Sleep apnea is a sleep-related condition characterized by the swelling or relaxation of throat muscles, causing a blockage in the upper airways. This obstruction interrupts normal breathing patterns during sleep. The current gold standard polysomnography (PSG) test for detecting sleep apnea is expensive, inconvenient, and lacks widespread availability for the general population. This underscores the need for more user-friendly and readily available solutions to diagnose sleep apnea. Collaborative efforts between Deep learning practitioners and healthcare professionals are essential to ensure that the developed techniques align with established clinical standards and contribute effectively to early diagnosis, prediction, and management of sleep apnea. This paper delves into recent studies on intelligent sleep apnea detection mechanisms. We emphasize the background of sleep apnea and the evolution of medical understanding and technological advancement. We demonstrated the diagnosis of sleep apnea through experts like medical professionals. We delve into recent studies on intelligent sleep apnea detection mechanisms using numerous deep-learning techniques and methodologies. In the transition, our attention is directed toward recent research that underscores the significance of integrating health-related features through classifier training.