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Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics
Uppsala University, Medicinska vetenskapsområdet, Faculty of Medicine, Department of Medical Sciences, Clinical Physiology.
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. , p. 49
Series
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 0282-7476 ; 1389
Keywords [en]
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
Keywords [sv]
Fysiologi
National Category
Physiology
Identifiers
URN: urn:nbn:se:uu:diva-4665ISBN: 91-554-6090-9 (print)OAI: oai:DiVA.org:uu-4665DiVA, id: diva2:165420
Public defence
2004-12-01, Akademiska sjukhuset, Robergsalen, ingång 40, Akademiska sjukhuset, Uppsala, 13:15
Opponent
Supervisors
Available from: 2004-11-09 Created: 2004-11-09 Last updated: 2018-01-13Bibliographically approved
List of papers
1. Assessment of respiratory system mechanics by artificial neural networks: an exploratory study
Open this publication in new window or tab >>Assessment of respiratory system mechanics by artificial neural networks: an exploratory study
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2001 (English)In: Journal of applied physiology, ISSN 8750-7587, E-ISSN 1522-1601, Vol. 90, no 5, p. 1817-1824Article in journal (Refereed) Published
Abstract [en]

We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANN(MOD)) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient (R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANN(BIO) showed a performance expressed by R = 0.40 and ANN(MOD) by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-92368 (URN)11299272 (PubMedID)
Available from: 2004-11-09 Created: 2004-11-09 Last updated: 2017-12-14Bibliographically approved
2. Estimating respiratory system compliance during mechanical ventilation using artificial neural networks
Open this publication in new window or tab >>Estimating respiratory system compliance during mechanical ventilation using artificial neural networks
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2003 (English)In: Veterinary Anaesthesia and Analgesia, ISSN 1467-2987, E-ISSN 1467-2995, Vol. 97, no 4, p. 1143-1148Article 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.

IMPLICATIONS:

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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-92369 (URN)10.1213/​01.ANE.0000077905.92474.82 (DOI)14500172 (PubMedID)
Available from: 2004-11-09 Created: 2004-11-09 Last updated: 2017-12-14Bibliographically approved
3. Measurement of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks
Open this publication in new window or tab >>Measurement of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks
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Manuscript (Other academic)
Identifiers
urn:nbn:se:uu:diva-92370 (URN)
Available from: 2004-11-09 Created: 2004-11-09 Last updated: 2010-01-13Bibliographically approved
4. Robustness of two different methods for estimating respiratory system compliance during mechanical ventilation
Open this publication in new window or tab >>Robustness of two different methods for estimating respiratory system compliance during mechanical ventilation
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Article in journal (Refereed) Submitted
Identifiers
urn:nbn:se:uu:diva-92371 (URN)
Available from: 2004-11-09 Created: 2004-11-09Bibliographically approved

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