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Estimating respiratory system compliance during mechanical ventilation using artificial neural networks
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Physiology.
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2003 (English)In: Veterinary Anaesthesia and Analgesia, ISSN 1467-2987, E-ISSN 1467-2995, Vol. 97, no 4, 1143-1148 p.Article in journal (Refereed) Published
Abstract [en]

In this study we evaluated whether a technology based on artificial neural networks (ANN) could estimate the static compliance (C(RS)) of the respiratory system, even in the absence of an end-inspiratory pause, during continuous mechanical ventilation. A porcine model of acute lung injury was used to provide recordings of different respiratory mechanics conditions. Each recording consisted of 10 or more consecutive breaths in volume-controlled mechanical ventilation, followed by a breath having an end-inspiratory pause used to calculate C(RS) according to the interrupter technique (IT). The volume-pressure loop of the breath immediately preceding the one with pause was given to the ANN for the training, together with the C(RS) separately calculated by the IT. The prospective phase consisted of giving only the loops to the trained ANN and comparing the results yielded by it to the compliance separately calculated by the investigators. Determination of measurement agreement between ANN and IT methods showed an error of -0.67 +/- 1.52 mL/cm H(2)O (bias +/- SD). We could conclude that ANN, during volume-controlled mechanical ventilation, can extract C(RS) without needing to stop inspiratory flow.


We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.

Place, publisher, year, edition, pages
2003. Vol. 97, no 4, 1143-1148 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-92369DOI: 10.1213/​01.ANE.0000077905.92474.82PubMedID: 14500172OAI: oai:DiVA.org:uu-92369DiVA: diva2:165417
Available from: 2004-11-09 Created: 2004-11-09 Last updated: 2013-09-25Bibliographically approved
In thesis
1. Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics
Open this publication in new window or tab >>Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics
2004 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of this thesis was to test the capability of Artificial Neural Networks (ANN) to estimate respiratory mechanics during mechanical ventilation (MV). ANNs are universal function approximators and can extract information from complex signals.

We evaluated, in an animal model of acute lung injury, whether ANN can assess respiratory system resistance (RRS) and compliance (CRS) using the tracings of pressure at airways opening (PAW), inspiratory flow (V’) and tidal volume, during an end-inspiratory hold maneuver (EIHM). We concluded that ANN can estimate CRS and RRS during an EIHM. We also concluded that the use of tracings obtained by non-biological models in the learning process has the potential of substituting biological recordings.

We investigated whether ANN can extract CRS using tracings of PAW and V’, without any intervention of an inspiratory hold maneuver during continuous MV. We concluded that CRS can be estimated by ANN during volume control MV, without the need to stop inspiratory flow.

We tested whether ANN, fed by inspiratory PAW and V’, are able to measure static total positive end-expiratory pressure (PEEPtot,stat) during ongoing MV. In an animal model we generated dynamic pulmonary hyperinflation by shortening expiratory time. Different levels of external PEEP (PEEPAPP) were applied. Results showed that ANN can estimate PEEPtot,stat reliably, without any influence from the level of PEEPAPP.

We finally compared the robustness of ANN and multi-linear fitting (MLF) methods in extracting CRS when facing signals corrupted by perturbations. We observed that during the application of random noise, ANN and MLF maintain a stable performance, although in these conditions MLF may show better results. ANN have more stable performance and yield a more robust estimation of CRS than MLF in conditions of transient sensor disconnection.

We consider ANN to be an interesting technique for the assessment of respiratory mechanics.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2004. 49 p.
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 0282-7476 ; 1389
Physiology, artificial neural networks, respiratory system compliance, respiratory system resistance, respiratory system time constant, intrinsic positive end expiratory pressure, PEEPi, animal model, acute lung injury, oleic acid, Otis model, Fysiologi
National Category
urn:nbn:se:uu:diva-4665 (URN)91-554-6090-9 (ISBN)
Public defence
2004-12-01, Akademiska sjukhuset, Robergsalen, ingång 40, Akademiska sjukhuset, Uppsala, 13:15
Available from: 2004-11-09 Created: 2004-11-09Bibliographically approved

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