Zhu Kaiyin, Li Michael, Akbarian Sina, Hafezi Maziar, Yadollahi Azadeh, Taati Babak
1KITEToronto Rehabilitation Institute, University Health NetworkTorontoONM5G 2A2Canada.
2Institute of Biomaterial and Biomedical Engineering, University of TorontoTorontoONM5S 3G9Canada.
IEEE J Transl Eng Health Med. 2019 Oct 14;7:1900708. doi: 10.1109/JTEHM.2019.2946147. eCollection 2019.
A reliable, accessible, and non-intrusive method for tracking respiratory and heart rate is important for improving monitoring and detection of sleep apnea. In this study, an algorithm based on motion analysis of infrared video recordings was validated in 50 adults referred for clinical overnight polysomnography (PSG). The algorithm tracks the displacements of selected feature points on each sleeping participant and extracts respiratory rate using principal component analysis and heart rate using independent component analysis. For respiratory rate estimation (mean ± standard deviation), 89.89 % ± 10.95 % of the overnight estimation was accurate within 1 breath per minute compared to the PSG-derived respiratory rate from the respiratory inductive plethysmography signal, with an average root mean square error (RMSE) of 2.10 ± 1.64 breaths per minute. For heart rate estimation, 77.97 % ± 18.91 % of the overnight estimation was within 5 beats per minute of the heart rate derived from the pulse oximetry signal from PSG, with mean RMSE of 7.47 ± 4.79 beats per minute. No significant difference in estimation of RMSE of either signal was found according to differences in body position, sleep stage, or amount of the body covered by blankets. This vision-based method may prove suitable for overnight, non-contact monitoring of respiratory rate. However, at present, heart rate monitoring is less reliable and will require further work to improve accuracy.
一种可靠、可及且非侵入性的呼吸和心率追踪方法对于改善睡眠呼吸暂停的监测和检测至关重要。在本研究中，一种基于红外视频记录运动分析的算法在50名被转诊进行临床夜间多导睡眠图（PSG）检查的成年人中得到验证。该算法追踪每个睡眠参与者身上选定特征点的位移，并使用主成分分析提取呼吸频率，使用独立成分分析提取心率。对于呼吸频率估计（均值±标准差），与通过呼吸感应体积描记法信号从PSG得出的呼吸频率相比，夜间估计的89.89%±10.95%在每分钟1次呼吸范围内准确，平均均方根误差（RMSE）为每分钟2.10±1.64次呼吸。对于心率估计，夜间估计的77.97%±18.91%在PSG的脉搏血氧饱和度信号得出的心率每分钟5次心跳范围内，平均RMSE为每分钟7.47±4.79次心跳。根据身体位置、睡眠阶段或被毯子覆盖的身体部位的差异，未发现两种信号的RMSE估计有显著差异。这种基于视觉的方法可能适用于夜间非接触式呼吸频率监测。然而，目前心率监测不太可靠，需要进一步开展工作以提高准确性。
