Fan Shu-Han, Chou Chia-Ching, Chen Wei-Chen, Fang Wai-Chi
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5989-92. doi: 10.1109/EMBC.2015.7319756.
In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.
在本研究中，提出了一种基于心电图衍生呼吸（EDR）和心率变异性（HRV）频率分析的有效实时阻塞性睡眠呼吸暂停（OSA）检测方法。与需要从患者身上测量多种生理信号的传统多导睡眠图（PSG）相比，所提出的OSA检测方法仅使用心电图信号来确定OSA的时间间隔。为了在硬件中可行地实现以实现实时检测和便携式应用，本研究利用简化的Lomb周期图对EDR和HRV进行频率分析。这项工作的实验结果表明，通过整合EDR和HRV指标，总体准确率可有效提高，特异性（Sp）为91%，灵敏度（Se）为95.7%，准确率为93.2%。
