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Improving the Accuracy and Efficiency of Respiratory Rate Measurements in Children Using Mobile Devices

Author

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  • Walter Karlen
  • Heng Gan
  • Michelle Chiu
  • Dustin Dunsmuir
  • Guohai Zhou
  • Guy A Dumont
  • J Mark Ansermino

Abstract

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

Suggested Citation

  • Walter Karlen & Heng Gan & Michelle Chiu & Dustin Dunsmuir & Guohai Zhou & Guy A Dumont & J Mark Ansermino, 2014. "Improving the Accuracy and Efficiency of Respiratory Rate Measurements in Children Using Mobile Devices," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
  • Handle: RePEc:plo:pone00:0099266
    DOI: 10.1371/journal.pone.0099266
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    Citations

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    Cited by:

    1. Shahreen Raihana & Dustin Dunsmuir & Tanvir Huda & Guohai Zhou & Qazi Sadeq-ur Rahman & Ainara Garde & Md Moinuddin & Walter Karlen & Guy A Dumont & Niranjan Kissoon & Shams El Arifeen & Charles Larso, 2015. "Development and Internal Validation of a Predictive Model Including Pulse Oximetry for Hospitalization of Under-Five Children in Bangladesh," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-15, November.
    2. Stephanie Baker & Wei Xiang & Ian Atkinson, 2021. "Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.

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