Literature DB >> 25402668

A pilot study of the nocturnal respiration rates in COPD patients in the home environment using a non-contact biomotion sensor.

Tarig Ballal1, Conor Heneghan, Alberto Zaffaroni, Patricia Boyle, Philip de Chazal, Redmond Shouldice, Walter T McNicholas, Seamas C Donnelly.   

Abstract

Nocturnal respiration rate parameters were collected from 20 COPD subjects over an 8 week period, to determine if changes in respiration rate were associated with exacerbations of COPD. These subjects were primarily GOLD Class 2 to 4, and had been recently discharged from hospital following a recent exacerbation. The respiration rates were collected using a non-contact radio-frequency biomotion sensor which senses respiratory effort and body movement using a short-range radio-frequency sensor. An adaptive notch filter was applied to the measured signal to determine respiratory rate over rolling 15 s segments. The accuracy of the algorithm was initially verified using ten manually-scored 15 min segments of respiration extracted from overnight polysomnograms. The calculated respiration rates were within 1 breath min(-1) for >98% of the estimates. For the 20 subjects monitored, 11 experienced one or more subsequent exacerbation of COPD (ECOPD) events during the 8 week monitoring period (19 events total). Analysis of the data revealed a significant increase in nocturnal respiration rate (e.g. >2 breath min(-1)) prior to many ECOPD events. Using a simple classifier of a change of 1 breath min(-1) in the mode of the nocturnal respiration rate, a predictive rule showed a sensitivity of 63% and specificity of 85% for predicting an exacerbation within a 5 d window. We conclude that it is possible to collect respiration rates reliably in the home environment, and that the respiration rate may be a potential indicator of change in clinical status.

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Year:  2014        PMID: 25402668     DOI: 10.1088/0967-3334/35/12/2513

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  4 in total

1.  Mean nocturnal respiratory rate predicts cardiovascular and all-cause mortality in community-dwelling older men and women.

Authors:  Mathias Baumert; Dominik Linz; Katie Stone; R Doug McEvoy; Steve Cummings; Susan Redline; Reena Mehra; Sarah Immanuel
Journal:  Eur Respir J       Date:  2019-07-25       Impact factor: 16.671

2.  Performance of Contactless Respiratory Rate Monitoring by Albus HomeTM, an Automated System for Nocturnal Monitoring at Home: A Validation Study.

Authors:  William Do; Richard Russell; Christopher Wheeler; Megan Lockwood; Maarten De Vos; Ian Pavord; Mona Bafadhel
Journal:  Sensors (Basel)       Date:  2022-09-21       Impact factor: 3.847

3.  Machine-learning based feature selection for a non-invasive breathing change detection.

Authors:  Juliana Alves Pegoraro; Sophie Lavault; Nicolas Wattiez; Thomas Similowski; Jésus Gonzalez-Bermejo; Etienne Birmelé
Journal:  BioData Min       Date:  2021-07-18       Impact factor: 2.522

4.  Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

Authors:  Roxana Tiron; Graeme Lyon; Hannah Kilroy; Ahmed Osman; Nicola Kelly; Niall O'Mahony; Cesar Lopes; Sam Coffey; Stephen McMahon; Michael Wren; Kieran Conway; Niall Fox; John Costello; Redmond Shouldice; Katharina Lederer; Ingo Fietze; Thomas Penzel
Journal:  J Thorac Dis       Date:  2020-08       Impact factor: 3.005

  4 in total

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