PT - JOURNAL ARTICLE AU - Gordon B. Drummond AU - Darius Fischer AU - Margaret Lees AU - Andrew Bates AU - Janek Mann AU - D.K. Arvind TI - Classifying signals from a wearable accelerometer device to measure respiratory rate AID - 10.1183/23120541.00681-2020 DP - 2021 Apr 01 TA - ERJ Open Research PG - 00681-2020 VI - 7 IP - 2 4099 - http://openres.ersjournals.com/content/7/2/00681-2020.short 4100 - http://openres.ersjournals.com/content/7/2/00681-2020.full SO - erjor2021 Apr 01; 7 AB - Background 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.Methods 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.Results 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.Conclusion Signals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.A machine learning method was developed to classify sections of breathing records from acutely ill patients wearing a small wireless motion sensor. This would allow accurate and automatic measurement, recording, and charting of respiratory rate. https://bit.ly/301P8XW