Montazeri Ghahjaverestan Nasim, Fan Wei, Aguiar Cristiano, Yu Jackson, Bradley T Douglas
Sleep Research Laboratory of the University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada.
Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Nat Sci Sleep. 2022 Jul 1;14:1213-1223. doi: 10.2147/NSS.S360970. eCollection 2022.
Due to lack of access and high cost of polysomnography, portable sleep apnea testing has been developed to diagnose sleep apnea. Despite being less expensive, and having fewer sensors and reasonable accuracy in identifying sleep apnea, such devices can be less accurate than polysomnography in detecting apneas/hypopneas. To increase the accuracy of apnea/hypopnea detection, an accurate airflow estimation is required. However, current airflow measurement techniques employed in portable devices are inconvenient and subject to displacement during sleep. In this study, algorithms were developed to estimate respiratory motion and airflow using tracheo-sternal motion and tracheal sounds.
Adults referred for polysomnography were included. Simultaneous to polysomnography, a patch device with an embedded 3-dimensional accelerometer and microphone was affixed to the suprasternal notch to record tracheo-sternal motion and tracheal sounds, respectively. Tracheo-sternal motion was used to train two mathematical models for estimating changes in respiratory motion and airflow compared to simultaneously measured thoracoabdominal motion and nasal pressure from polysomnography. The amplitude of the estimated airflow was then adjusted by the tracheal sound envelope in segments with unstable breathing.
Two hundred and fifty-two subjects participated in this study. Overall, the algorithms provided highly accurate estimates of changes in respiratory motion and airflow with mean square errors (MSE) of 3.58 ± 0.82% and 2.82 ± 0.71%, respectively, compared to polysomnographic signals. The estimated motion and airflow from the patch signals detected apneas and hypopneas scored on polysomnography in 63.9% and 88.3% of cases, respectively.
This study presents algorithms to accurately estimate changes in respiratory motion and airflow, which provides the ability to detect respiratory events during sleep. Our study suggests that such a simple and convenient method could be used for portable monitoring to detect sleep apnea. Further studies will be required to test this possibility.
由于多导睡眠图检查难以获得且成本高昂,便携式睡眠呼吸暂停检测设备应运而生,用于诊断睡眠呼吸暂停。尽管此类设备成本较低、传感器较少,且在识别睡眠呼吸暂停方面具有合理的准确性,但在检测呼吸暂停/低通气方面,其准确性可能低于多导睡眠图检查。为提高呼吸暂停/低通气检测的准确性,需要进行准确的气流估计。然而,目前便携式设备中采用的气流测量技术不便,且在睡眠期间容易发生位移。在本研究中,开发了利用胸骨上窝运动和气管声音来估计呼吸运动和气流的算法。
纳入转诊进行多导睡眠图检查的成年人。在进行多导睡眠图检查的同时,将一个嵌入三维加速度计和麦克风的贴片装置贴于胸骨上切迹,分别记录胸骨上窝运动和气管声音。利用胸骨上窝运动训练两个数学模型,以估计与多导睡眠图检查同时测量的胸腹部运动和鼻压力相比的呼吸运动和气流变化。然后,在呼吸不稳定的时间段内,通过气管声音包络调整估计气流的幅度。
252名受试者参与了本研究。总体而言,与多导睡眠图信号相比,算法对呼吸运动和气流变化的估计具有高度准确性,均方误差分别为3.58±0.82%和2.82±0.71%。从贴片信号估计的运动和气流分别在63.9%和88.3%的病例中检测到了多导睡眠图检查记录的呼吸暂停和低通气。
本研究提出了准确估计呼吸运动和气流变化的算法,能够在睡眠期间检测呼吸事件。我们的研究表明,这种简单便捷的方法可用于便携式监测以检测睡眠呼吸暂停。需要进一步研究来验证这种可能性。