Drummond Gordon B, Fischer Darius, Lees Margaret, Bates Andrew, Mann Janek, Arvind D K
Dept of Anaesthesia, Critical Care, and Pain Medicine, University of Edinburgh, Edinburgh, UK.
Centre for Speckled Computing, School of Informatics, University of Edinburgh, Edinburgh, UK.
ERJ Open Res. 2021 Apr 26;7(2). doi: 10.1183/23120541.00681-2020. eCollection 2021 Apr.
Automatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60 s is needed for adequate precision.
We studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a triaxial accelerometer attached to the chest wall and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula.
In the test set of patients, machine classification of the respiratory signal reduced the median absolute difference (interquartile range) from 1.25 (0.56-2.18) to 0.48 (0.30-0.78) breaths per min. 50% of the recording periods were rejected as unreliable and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6 min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time.
Signals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.
在综合医院患者中自动测量呼吸频率具有挑战性。患者的活动会使信号质量下降，且呼吸周期的变化意味着需要精确观察至少60秒才能获得足够的精度。
我们对一家教学医院最近收治的急性病患者进行了研究。通过连接在胸壁上的三轴加速度计测量呼吸持续时间，并与鼻导管传来的信号进行比较。我们将患者记录随机分为训练集（n = 54）和测试集（n = 7）。我们使用机器学习来训练神经网络，以选择可靠的信号，自动识别与准确测量呼吸频率相关的信号特征。我们使用测试记录来评估该设备的准确性，以加速度计和鼻导管提供的呼吸频率之间的中位数绝对差来表示。
在患者测试集中，呼吸信号的机器分类将中位数绝对差（四分位间距）从每分钟1.25次（0.56 - 2.18）降至0.48次（0.30 - 0.78）。50%的记录时段被判定为不可靠而被排除，在一名患者中，只有10%的信号时间被分类为可靠。然而，即使只有10%的观察时间，在一小时的记录中也能实现6分钟的准确测量，比基于更少观察时间的护士记录更可靠。
佩戴在身体上的加速度计发出的信号能够准确测量呼吸频率，这可以改善成年住院患者的自动疾病评分。
