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Titolo:
Assessment of respiratory system mechanics by artificial neural networks: an exploratory study
Autore:
Perchiazzi, G; Hogman, M; Rylander, C; Giuliani, R; Fiore, T; Hedenstierna, R;
Indirizzi:
Univ Bari, Dept Emergency & Transplantat, I-70124 Bari, Italy Univ Bari Bari Italy I-70124 ergency & Transplantat, I-70124 Bari, Italy Univ Uppsala Hosp, Dept Clin Physiol, S-75185 Uppsala, Sweden Univ UppsalaHosp Uppsala Sweden S-75185 hysiol, S-75185 Uppsala, Sweden Sahlgrens Univ Hosp, Dept Anaesthesia, S-41345 Gothenburg, Sweden Sahlgrens Univ Hosp Gothenburg Sweden S-41345 S-41345 Gothenburg, Sweden
Titolo Testata:
JOURNAL OF APPLIED PHYSIOLOGY
fascicolo: 5, volume: 90, anno: 2001,
pagine: 1817 - 1824
SICI:
8750-7587(200105)90:5<1817:AORSMB>2.0.ZU;2-V
Fonte:
ISI
Lingua:
ENG
Soggetto:
RECOGNITION; VENTILATION; PREDICTION; PATTERN; FLOW;
Keywords:
resistance; compliance; oleic acid; acute lung injury;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
27
Recensione:
Indirizzi per estratti:
Indirizzo: Perchiazzi, G Univ Bari, Osped Policlin, Ist Anestesia & Rianimaz, Piazza Giulio Cesare,I-70124 Bari, Italy Univ Bari Piazza Giulio Cesare Bari Italy I-70124 ri, Italy
Citazione:
G. Perchiazzi et al., "Assessment of respiratory system mechanics by artificial neural networks: an exploratory study", J APP PHYSL, 90(5), 2001, pp. 1817-1824

Abstract

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 EAsimulations. 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.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 09/07/20 alle ore 01:31:04