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Multivariable Regression Model to Estimate Tidal Volume for Different Respiratory Patterns

文献类型： 会议论文

第一作者： Daniel Romero Pérez

作者： Daniel Romero Pérez 1 ; Jordi Solà Soler 1 ; Leon Balchin 2 ; Arantxa Mas Serra 3 ; Manuel Lujan Torné 4 ; Melinda R. Popoviciu Koborzan 3 ; Beatriz F. Giraldo 1 ;

作者机构： 1.Automatic Control Dept. (ESAII), the Barcelona East School of Engineering (EEBE), Universitat Politècnica de Catalunya (UPC), Institute for Bioengineering of Catalonia (IBEC) – The Barcelona Institute of Science and Technology, and CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain

2.ESAII, Universitat Politècnica de Catalunya (UPC), Institute for Bioengineering of Catalonia (IBEC)

3.Intensive Medicine Service, Moisès Broggi Hospital, Sant Joan d’Espi, Barcelona, Spain

4.Pneumology Service, Parc Tauli University Hospital, Universitat Autònoma de Barcelona

关键词： Solid modeling;Biological system modeling;Nose;Tin;Predictive models;Recording;Calibration

会议名称： Annual International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 1-4

摘要： Respiratory patterns present great variability, both in healthy subjects and in patients with different diseases and forms of nasal, oral, superficial or deep breathing. The analysis of this variability depends, among others, on the device used to record the signals that describe these patterns. In this study, we propose multivariable regression models to estimate tidal volume (VT) considering different breathing patterns. Twenty-three healthy volunteers underwent continuous multisensor recordings considering different modes of breathing. Respiratory flow and volume signals were recorded with a pneumotachograph and thoracic and abdominal respiratory inductive plethysmographic bands. Several respiratory parameters were extracted from the volume signals, such as inspiratory and expiratory areas (Areains, Areaexp), maximum volume relative to the cycle start and end (VTins, VTexp), inspiratory and expiratory time (Tins, Texp), cycle duration (Ttot), and normalized parameters of clinical interest. The parameters with the greatest individual predictive power were combined using multivariable models to estimate VT. Their performance were quantified in terms of determination coefficient (R2), relative error (ER) and interquartile range (IQR). Using only three parameters, the results obtained for the thoracic band (VTexp, Ttot, Areaexp) were better than those obtained from the abdominal band (VTexp, Tins, Areains) with R2 = 0.94 (IQR: 0.07); ER = 6.99 (IQR: 6.12) vs R2 = 0.91 (IQR: 0.09), ER = 8.70 (IQR: 4.62). Overall performance increased to R2 = 0.97 (IQR: 0.02) and ER = 4.60 (IQR: 3.68) when parameters from the different bands were combined, further improving when was applied to segments with different inspiration–expiration patterns. In particular, the nose-nose ER = 1.39 (IQR: 0.73), nose-mouth ER = 2.11 (IQR: 1.23) and mouth-mouth ER = 2.29 (IQR: 1.44) patterns showed the best results compared to those obtained for basal, shallow and deep breathing.Clinical relevance— Respiratory pattern variability can be described using multivariable regression model for tidal volume.

分类号： q81

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