Accurate contactless sleep apnea detection framework with signal processing and machine learning methods

The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. Recent studies have shown that radar-based non-contact vital sign monitoring system can effectively detect sleep apnea. However, the detection accuracy in the current study still needs to be improved. In this paper, we propose a sleep apnea detection framework based on FMCW radar. First, the radar system is employed to record the sleep data throughout the night with polysomnography (PSG) comparison. Then, in order to extract more accurate respiratory signal from the raw radar data, the signal processing methods are investigated to solve the observed discontinuity phenomenon. Finally, machine learning methods are adopted. The apneic and not-apneic events are classified accurately by selecting effective features of respiratory signal. As shown in the experimental results, the proposed system could achieve a good classification perfor-mance with an accuracy of 95.53%, a sensitivity of 72.60%, a specificity of 97.32%, a Kappa of 0.68, and an F -score of 0.84.

Automated summary

The detection of sleep apnea is crucial for evaluating sleep quality and diagnosing diseases. This paper proposes a framework for sleep apnea detection based on FMCW radar, which utilizes signal processing methods and machine learning techniques to improve detection accuracy. Experimental results demonstrate that the proposed system achieves good classification performance.