Obstructive Sleep Apnea (OSA) Events Classification by Effective Radar Cross Section (ERCS) Method Using Microwave Doppler Radar and Machine Learning Classifier

Abstract

In-home sleep monitoring system using Microwave Doppler radar is gaining attention as it is unobtrusive and noncontact form of measurement. Most of the reported results in literature focused on utilizing radar-reflected signal amplitude to recognize Obstructive sleep apnea (OSA) events which requires iterative analysis and cannot recommend about sleep positions also (supine, prone and side). In this paper, we propose a new, robust and automated ERCS-based (Effective Radar Cross section) method for classifying OSA events (normal, apnea and hypopnea) by integrating radar system in a clinical setup. In our prior attempt, ERCS has been proven versatile method to recognize different sleep postures. We also employed two different machine learning classifiers (K-nearest neighbor (KNN) and Support Vector machine (SVM) to recognize OSA events from radar captured ERCS and breathing rate measurement from five different patients' clinical study. SVM with quadratic kernel outperformed with other classifiers with an accuracy of 96.7 % for recognizing different OSA events. The proposed system has several potential applications in healthcare, continuous monitoring and security/surveillance applications.