Paper Contents
Abstract
Sleep apnea is a prevalent sleep disorder characterized by pauses in breathing or shallow breaths during sleep, often leading to fragmented sleep and various health risks. Early detection and continuous monitoring are crucial for effective management and prevention of complications. This paper proposes an AI-powered vigilance system leveraging machine learning techniques to anticipate sleep apnea episodes. By analyzing patterns in physiological data such as heart rate variability, oxygen saturation levels, and respiratory patterns obtained from wearable sensors or home monitoring devices, the system aims to predict and alert users to potential apnea events in real-time. Keywords: sleep apnea, machine learning, AI vigilance, physiological data analysis, real-time monitoring.
Copyright
Copyright © 2025 Dr.AB.Hajira Be. This is an open access article distributed under the Creative Commons Attribution License.
