Innovative Machine Learning Approaches for Real-Time Detection of Obstructive Sleep Apnea Using ECG Signals in Medical Wireless Systems

This study presents a novel method for the real-time detection of obstructive sleep apnea (OSA) using ECG signal processing in wireless medical systems. Utilizing data from the Physionet Apnea-ECG database, the model incorporates both novel and established features, achieving an accuracy of 88.4% through advanced signal processing and machine learning techniques. To address challenges such as interference and data loss in wireless ECG monitoring, optimized data compression, error correction, and encrypted wireless sensor networks were implemented, with integration into mobile and cloud platforms. Future research will explore the use of terahertz frequencies to enhance data rates and facilitate non-invasive monitoring in remote environments. These findings underscore the potential of wireless systems to improve real-time health monitoring, thereby advancing telemedicine and personalized healthcare through more accurate and reliable patient monitoring.