ENGINEERING APPROACHES IN THE DIAGNOSIS OF SLEEP APNEA

Sleep apnea is a sleep disorder that significantly affects human life and occurs as a result of repeated obstructions in the respiratory system lasting at least 10 seconds during sleep. The most common type, Obstructive Sleep Apnea (OSA), affects the upper respiratory tract, whereas Central Sleep Apnea (CSA) occurs due to dysfunction in the respiratory control center in the brain. Sleep apnea manifests with symptoms such as fatigue upon awakening, snoring, and daytime sleepiness. If left untreated, it may lead to serious health complications including stroke, cardiovascular diseases, and hypertension. Polysomnography (PSG) is the most widely used diagnostic method for sleep apnea. However, this test involves several limitations in terms of time consumption, patient comfort, and financial cost. Therefore, there is an increasing need for alternative engineering-based diagnostic support methods to complement polysomnography. Recent advancements in Biomedical Engineering, Electrical and Electronics Engineering, and Software Engineering have enabled the development of portable, cost-effective, and highly compatible systems for sleep apnea detection. Sleep apnea can be identified through the processing of physiological signals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and oxygen saturation levels. The acquired data are analyzed using artificial intelligence techniques and machine learning algorithms, which have become prominent tools in biomedical signal analysis. Furthermore, the integration of wearable devices and Internet of Things (IoT)-based technologies allows continuous monitoring of patients in home environments. This study discusses the significance of engineering-based solutions in sleep apnea diagnosis and highlights their contributions to modern healthcare technologies.