Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation

Yeong Shiong Chiew; Christopher G. Pretty; Alex Beatson; Daniel Glassenbury; Vincent Major; Simon Corbett; Daniel Redmond; Akos Szlavecz; Geoffrey M. Shaw; J. Geoffrey Chase

doi:10.1109/EMBC.2015.7319591

Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation

Yeong Shiong Chiew, Christopher G. Pretty, Alex Beatson, Daniel Glassenbury, Vincent Major, Simon Corbett, Daniel Redmond, Akos Szlavecz, Geoffrey M. Shaw, J. Geoffrey Chase

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

10 Citations (Scopus)

Abstract

Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.

Original language	English
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	5315-5318
Number of pages	4
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7319591
Publication status	Published - 4 Nov 2015
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2015 - Milan, Italy Duration: 25 Aug 2015 → 29 Aug 2015 Conference number: 37th https://embc.embs.org/2015/ https://ieeexplore.ieee.org/xpl/conhome/7302811/proceeding (Proceedings)

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2015-November
ISSN (Print)	1557-170X

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2015
Abbreviated title	EMBC 2015
Country/Territory	Italy
City	Milan
Period	25/08/15 → 29/08/15
Internet address	https://embc.embs.org/2015/ https://ieeexplore.ieee.org/xpl/conhome/7302811/proceeding (Proceedings)

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10.1109/EMBC.2015.7319591

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Chiew, Y. S., Pretty, C. G., Beatson, A., Glassenbury, D., Major, V., Corbett, S., Redmond, D., Szlavecz, A., Shaw, G. M., & Chase, J. G. (2015). Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 5315-5318). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2015.7319591

Chiew, Yeong Shiong ; Pretty, Christopher G. ; Beatson, Alex et al. / Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 5315-5318 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation",

abstract = "Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.",

author = "Chiew, {Yeong Shiong} and Pretty, {Christopher G.} and Alex Beatson and Daniel Glassenbury and Vincent Major and Simon Corbett and Daniel Redmond and Akos Szlavecz and Shaw, {Geoffrey M.} and Chase, {J. Geoffrey}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; International Conference of the IEEE Engineering in Medicine and Biology Society 2015, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",

year = "2015",

month = nov,

day = "4",

doi = "10.1109/EMBC.2015.7319591",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015",

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Chiew, YS, Pretty, CG, Beatson, A, Glassenbury, D, Major, V, Corbett, S, Redmond, D, Szlavecz, A, Shaw, GM & Chase, JG 2015, Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2015-November, IEEE, Institute of Electrical and Electronics Engineers, pp. 5315-5318, International Conference of the IEEE Engineering in Medicine and Biology Society 2015, Milan, Italy, 25/08/15. https://doi.org/10.1109/EMBC.2015.7319591

Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation. / Chiew, Yeong Shiong; Pretty, Christopher G.; Beatson, Alex et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 5315-5318 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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T1 - Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation

AU - Chiew, Yeong Shiong

AU - Pretty, Christopher G.

AU - Beatson, Alex

AU - Glassenbury, Daniel

AU - Major, Vincent

AU - Corbett, Simon

AU - Redmond, Daniel

AU - Szlavecz, Akos

AU - Shaw, Geoffrey M.

AU - Chase, J. Geoffrey

N1 - Conference code: 37th

PY - 2015/11/4

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N2 - Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.

AB - Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.

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BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - International Conference of the IEEE Engineering in Medicine and Biology Society 2015

Y2 - 25 August 2015 through 29 August 2015

ER -

Chiew YS, Pretty CG, Beatson A, Glassenbury D, Major V, Corbett S et al. Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 5315-5318. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2015.7319591

Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation

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