Analysis of Efficient Biometric Index using Heart Rate Variability for Remote Monitoring of Obstructive Sleep Apnea

Abstract

Objective: Obstructive sleep apnea (OSA) associated health problems are undiagnosed due to the expensive and realistic limitations of overnight PSG examination. In this research, an accurate and resource-efficient biometric index is proposed for remote monitoring of the OSA patients having universal property at all body postures. Methods: In this study, the analysis of 44 subjects determines that the Heart Rate Variability (HRV) parameters including time and frequency domains such as ratio of Standard Deviation of NN interval (SDNN) and Root-Mean-Squared of the Successive Differences (RMSSD), termed as SRR; and LHR (ratio between normalized Low to High-Frequency power) can be taken into consideration to develop reasonable accurate and cost-efficient indexes for OSA levels measurement. Results: The experimental results elaborate that the OSA is considered to be severe (SRR ≥ 1.8, LHR ≥ 1.6), moderate (1.45 < LHR ≥ 1.6), mild (0.4 < LHR ≥ 0.9), and normal (0.4 ≥ SRR, 0.1 ≥ LHR). Additionally, it is observed that SRR and LHR indexes have an accuracy of 95.83% and 91.66% respectively for diagnosing OSA. Conclusion: This study concludes that SRR is easy to compute, guarantee a high degree of accuracy, resource efficient and ensure better correlation (with RDI and AHI) than LHR. Therefore, this study suggests that SRR obtained from wearable devices can be utilized as a convenient index for remote monitoring of OSA levels.