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Nonconstrained Sleep Monitoring System and Algorithms Using Air-Mattress With Balancing Tube Method

机译:气管平衡管法无约束睡眠监测系统及算法

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We evaluated the performance of a bed-type sensor system using the air-mattress with balancing tube (AMBT) method to noninvasively monitor the signals of heartbeat, respiration, and events of snoring, sleep apnea and body movement of subject on the system. The proposed system consists of multiple cylindrical air cells, two sensor cells and 18 support cells, and the small physiological signals were measured by the changes in pressure difference between the sensor cells, and the dc component was removed by balancing tube that is connecting the sensor cells. Using newly developed AMBT method, heartbeat, respiration, snoring, and body movement signals were clearly measured. For the concept of a home healthcare system, two automatic processing algorithms were developed: one is to estimate the mean heart and respiration rates for every 30 s, and another one is to detect the snoring, sleep apnea, and body movement events from the measured signals. In the beat-to-beat heart rate and breath-by-breath respiration rate analyses, the correlation coefficients of the heart and respiration rates from the proposed AMBT method compared with reference methods, electrocardiogram, and respiration effort signal from piezoelectric belt, were 0.98 ( $p$ ≪ 0.01) and 0.96 ($p$ ≪ 0.01), respectively. Sensitivity and positive predictive value (PPV) of the detection algorithm for snoring event were 93%, 96%, for sleep apnea event were 93%, 88%, and for body movement event were 86%, 100%, respectively. These findings support that ABMT method provides an accurate and reliable means to monitor heartbeat, respiration activities and the sleep events during sleep.
机译:我们使用平衡管气垫(AMBT)方法评估了床型传感器系统的性能,以无创地监测系统上对象的心跳,呼吸信号以及打,睡眠呼吸暂停和主体运动的事件。拟议的系统由多个圆柱形空气电池,两个传感器电池和18个支持电池组成,通过传感器电池之间压力差的变化来测量较小的生理信号,并通过连接传感器的平衡管除去直流分量。细胞。使用新开发的AMBT方法,可以清楚地测量心跳,呼吸,打和身体运动的信号。对于家庭医疗保健系统的概念,开发了两种自动处理算法:一种是估计每30 s的平均心脏和呼吸频率,另一种是从测量的值中检测出打nor,睡眠呼吸暂停和身体运动事件信号。在逐搏心率和逐呼吸呼吸率分析中,与建议的参考方法,心电图和压电皮带的呼吸努力信号相比,拟议的AMBT方法对心脏和呼吸率的相关系数为0.98 ($ p $≪ 0.01)和0.96($ p $≪ 0.01)。打algorithm事件检测算法的敏感性和阳性预测值(PPV)分别为93%,96%,睡眠呼吸暂停事件为93%,88%和身体运动事件为86%,100%。这些发现支持ABMT方法提供了一种准确可靠的手段来监测睡眠期间的心跳,呼吸活动和睡眠事件。

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