Doheny Emer P, Lowery Madeleine M, Russell Audrey, Ryan Silke
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4668-4671. doi: 10.1109/EMBC44109.2020.9176573.
Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.
可穿戴惯性传感器提供了在睡眠期间监测睡眠姿势和呼吸率的可能性，从而实现一种舒适且低成本的远程监测患者的方法。本文提出了利用加速度计数据估计睡眠期间呼吸率和姿势的新方法，研究了两种传感器位置的算法性能，并比较了不同睡眠姿势下加速度计得出的呼吸率。11名参与者（9名男性；年龄：47.82±14.14岁；体重指数30.9±5.27kg/m；呼吸暂停低通气指数5.77±4.18）在进行预定的临床多导睡眠图（PSG）检查时，在其胸部和上腹部佩戴了一个三轴加速度计。PSG插管流量和位置数据用作呼吸率（每分钟呼吸次数，bpm）和姿势的基准数据。使用逻辑回归对睡眠姿势进行分类，其特征来自滤波后的加速度和方向。使用自适应峰值检测算法对30秒时段的加速度计得出的呼吸率进行估计，该算法结合滤波后的加速度和方向数据来识别个体呼吸。然后比较传感器得出的呼吸率和PSG呼吸率。呼吸率的平均绝对误差（MAE）在传感器位置之间没有差异（腹部：1.67±0.37bpm；胸部：1.89±0.53bpm；p=0.52），而与仰卧位（2.43±0.95bpm）相比，参与者侧卧时观察到MAE降低（1.58±0.54bpm），p<0.01。在所有受试者的所有30秒窗口中，83.6%的MAE小于2bpm。姿势分类器区分仰卧位和左/右侧时的受试者工作特征曲线下面积（ROC AUC）为0.87，区分左侧和右侧时的ROC AUC为0.94。所提出的方法可能为家庭长期睡眠姿势和呼吸监测提供一种低成本解决方案。
