Sleep Apnea

Abstract

Sleep is a basic activity of human life that reenergizes the body and mind, having a good sleep is a crucial aspect of an individual’s existence and health. Obstructive sleep apnea syndrome (OSAS) is a series of recurrent episodes of partial or complete blockage of breathing during sleep which causes affected people to wake up. Polysomnography (PSG) is the gold standard for diagnosing OSAS, it is a highly involved procedure. This project aims to leverage use of IoT sensors, particularly the ECG (Electrocardiogram) and SpO2 (Saturation of Peripheral Oxygen) sensors to record physiological data, including heart rate and oxygen saturation levels to address the complex procedure of PSG. This novel method includes use of a deep learning algorithm CNN combined with the use of sensors to detect the sleep apnea disease. To complete the AI (Artificial Intelligence) and IoT (Internet of Things) embedded infrastructure, A mobile application, ApneaSense is added to it to provide convenient and affordable at-home sleep screening option. This complete system ensures better health outcomes and more effective sleep apnea therapy in general. Through this easy to use, easily accessible and non- invasive system used as an early sleep apneic detection approach, ApneaSense let people take charge of their health condition. Through early detection of sleep apnea, risk of being unaware of the condition and consequences from untreated sleep apnea like heart issues, daily weariness, and cognitive decline lowers.