RT Journal Article
SR Electronic
T1 Classifying signals from a wearable accelerometer device to measure respiratory rate
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00681-2020
DO 10.1183/23120541.00681-2020
A1 Gordon B. Drummond
A1 Darius Fischer
A1 Margaret Lees
A1 Andrew Bates
A1 Janek Mann
A1 D.K. Arvind
YR 2021
UL http://openres.ersjournals.com/content/early/2021/02/25/23120541.00681-2020.abstract
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 at least 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 tri-axial 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 absolute difference from 1.25 (0.56 to 2.18) to 0.48 (0.30 to 0.78) breaths/min (median, interquartile range). 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.FootnotesThis manuscript has recently been accepted for publication in the ERJ Open Research. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article.Conflict of Interest: Dr. Drummond reports grants from Edinburgh and Lothians Health Foundation, during the conduct of the study.Conflict of Interest: Dr. Fischer has nothing to disclose.Conflict of Interest: Ms McDonald has nothing to disclose.Conflict of Interest: Dr. Bates has nothing to disclose.Conflict of Interest: Mr. Mann has nothing to disclose.Conflict of Interest: Pr. Arvind reports grants from Edinburgh and Lothians Health Foundation, during the conduct of the study; In addition, Pr. Arvind has a patent Method, Apparatus, Computer Program and System for Measuring Oscillatory Motion issued, and a patent Method, Apparatus, Computer Program and System for Measuring Oscillatory Motion issued.