Integration of AI and Iot Technologies for Non-Invasive Sleep Apnea Detection and Monitoring

Abstract

The prevalence of sleep apnea has driven demand for low-cost, non-invasive, and continuous home-based monitoring systems. This research paper presents an AI-enabled IoT architecture integrating wearable sensors, ESP32 microcontroller processing, and cloud-based analytics for real-time detection of apnea events. The system monitors physiological parameters including SpO2, heart rate, respiratory rate, and body movement. Machine learning models such as SVM, Random Forest, CNN, and LSTM enhance detection accuracy. Findings demonstrate 93% accuracy and 94% sensitivity, validating the effectiveness of the system as a scalable alternative to clinical polysomnography.