Influences of environmental noise level and respiration rate on the accuracy of acoustic respiration rate monitoring

J Clin Monit Comput. 2018 Feb;32(1):127-132. doi: 10.1007/s10877-017-9997-y. Epub 2017 Feb 7.

Abstract

We tested the hypothesis that the environmental noise generated by a forced-air warming system reduces the monitoring accuracy of acoustic respiration rate (RRa). Noise levels were adjusted to 45-55, 56-65, 66-75, and 76-85 dB. Healthy participants breathed at set respiration rates (RRset) of 6, 12, and 30/min. Under each noise level at each RRset, the respiration rates by manual counting (RRm) and RRa were recorded. Any appearance of the alarm display on the RRa monitor was also recorded. Each RRm of all participants agreed with each RRset at each noise level. At 45-55 dB noise, the RRa of 13, 17, and 17 participants agreed with RRset of 6, 12, and 30/min, respectively. The RRa of 14, 17, and 16 participants at 56-65 dB noise, agreed with RRset of 6, 12, and 30/min, respectively. At 66-75 dB noise, the RRa of 9, 15, and 16 participants agreed with RRset of 6, 12, and 30/min, respectively. The RRa of one, nine, and nine participants at 76-85 dB noise agreed with RRset of 6, 12, and 30/min, respectively, which was significantly less than the other noise levels (P < 0.05). Overall, 72.9% of alarm displays highlighted incorrect values of RRa. In a noisy situation involving the operation of a forced-air warming system, the acoustic respiration monitoring should be used carefully especially in patients with a low respiration rate.

Keywords: Acoustic respiration rate monitoring; Environmental sound; Forced-air warming system; Healthy participant.

MeSH terms

  • Acoustics
  • Adult
  • Body Mass Index
  • Clinical Alarms
  • Female
  • Healthy Volunteers
  • Heating / instrumentation
  • Humans
  • Intensive Care Units
  • Male
  • Middle Aged
  • Monitoring, Physiologic / instrumentation*
  • Monitoring, Physiologic / methods
  • Noise*
  • Operating Rooms
  • Respiration*
  • Respiratory Rate*
  • Signal Processing, Computer-Assisted
  • Time Factors