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Robustness of two different methods of monitoring respiratory system compliance during mechanical ventilation.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Hedenstierna laboratory. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care. Bari Univ, Dept Emergency & Organ Transplant, Bari, Italy.ORCID iD: 0000-0001-6834-6399
Sahlgrens Univ Hosp, Dept Anaesthesia & Intens Care Med, Gothenburg, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Hedenstierna laboratory. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care. Bari Univ, Dept Emergency & Organ Transplant, Bari, Italy.ORCID iD: 0000-0001-5668-7399
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Hedenstierna laboratory.ORCID iD: 0000-0002-0702-8343
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2017 (English)In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 55, no 10, p. 1819-1828Article in journal (Refereed) Published
Abstract [en]

Robustness measures the performance of estimation methods when they work under non-ideal conditions. We compared the robustness of artificial neural networks (ANNs) and multilinear fitting (MLF) methods in estimating respiratory system compliance (C RS) during mechanical ventilation (MV). Twenty-four anaesthetized pigs underwent MV. Airway pressure, flow and volume were recorded at fixed intervals after the induction of acute lung injury. After consecutive mechanical breaths, an inspiratory pause (BIP) was applied in order to calculate CRS using the interrupter technique. From the breath preceding the BIP, ANN and MLF had to compute CRS in the presence of two types of perturbations: transient sensor disconnection (TD) and random noise (RN). Performance of the two methods was assessed according to Bland and Altman. The ANN presented a higher bias and scatter than MLF during the application of RN, except when RN was lower than 2% of peak airway pressure. During TD, MLF algorithm showed a higher bias and scatter than ANN. After the application of RN, ANN and MLF maintain a stable performance, although MLF shows better results. ANNs have a more stable performance and yield a more robust estimation of C RS than MLF in conditions of transient sensor disconnection.

Place, publisher, year, edition, pages
2017. Vol. 55, no 10, p. 1819-1828
Keywords [en]
Acute lung injury, Lung compliance, Mechanical ventilation, Neural networks, Robustness
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-334171DOI: 10.1007/s11517-017-1631-0ISI: 000411111100009PubMedID: 28243966OAI: oai:DiVA.org:uu-334171DiVA, id: diva2:1158878
Funder
Swedish Heart Lung FoundationAvailable from: 2017-11-21 Created: 2017-11-21 Last updated: 2017-12-13Bibliographically approved

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Perchiazzi, GPellegrini, MariangelaLarsson, AndersHedenstierna, Göran
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