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A pulse transit time based fusion method for the noninvasive and continuous monitoring of respiratory rate

机译:基于脉冲转运时间的呼吸速率的非侵入性和连续监测的熔点融合方法

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The purpose of this study was to investigate whether pulse transit time (PTT) can be used for continuous monitoring of respiratory rate (RR). We derived PTT from the electrocardiogram and photoplethysmogram obtained from 42 recordings of CapnoBase, a publicly available benchmark data set for validating respiratory related measurements. The number of breaths in a minute (RR#) was estimated from the heart rate interval (HRI), pulse rate interval (PRI), and from PTT. In addition, to improve the estimation reliability, we investigated a fusion of the three HRI, PRI and PTT derived estimations. The root mean squared error (RSME) and a Bland-Altman plot were calculated using RR from capnography as reference. Finally, the proposed method was compared against the CapnoBase Smart Fusion RR benchmark estimation which estimates RR with three parameters extracted from the PPG signal alone. Thirty-seven recordings showed sufficient signal quality to estimate RR from PTT. The fused RR (RMSE 1.76 breaths/min) was more accurate than the estimations from PTT (RMSE 2.63 breaths/min), HRI (RMSE 1.96 breaths/min), and PRI (RMSE 2.73 breaths/min) alone. The proposed method also outperformed the CapnoBase benchmark (RMSE 3.08 breaths/min) algorithm. This study demonstrates that PTT is a valuable noninvasive parameter from which RR can be estimated.
机译:本研究的目的是研究脉冲转运时间(PTT)是否可用于连续监测呼吸率(RR)。我们从Capnobase的42个录像中获得的心电图和光学肉肌谱,用于验证呼吸相关测量的公开的基准数据。从心率间隔(HRI),脉搏间隔(PRI)和PTT估计一分钟(RR#)的呼吸次数。此外,为了提高估计可靠性,我们研究了三个HRI,PRI和PTT衍生估计的融合。使用CAPNography作为参考的RR计算均方根误差(RSME)和Bland-Altman图。最后,将所提出的方法与Capnobase智能融合RR基准估计进行比较,其估计来自单独的PPG信号中提取的三个参数的RR。三十七个录音显示出足够的信号质量来估算PTT的RR。融合的RR(RMSE 1.76呼吸/分钟)比PTT(RMSE 2.63呼吸/分钟),HRI(RMSE 1.96呼吸/分钟)和PRI(RMSE 2.73呼吸/分钟)更准确。所提出的方法还优于Capnobase基准(RMSE 3.08呼吸/分钟)算法。本研究表明PTT是可以估计RR的有价值的非侵入性参数。

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