Comput Methods Programs Biomed - Computer assessment of indirect insight during an airflow interrupter maneuver of breathing.


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The paper answers the questions if it is possible to conclude in objective way on more (than one -Rint - in a classical IT) number of parameters from the time domain post-interrupter signals during the occlusional measurement of respiratory mechanics and also verifies what accuracy can be achieved in such attempt. To obtain reported results, the time-domain enhanced interrupter technique (TD-EIT) was developed in this paper using computer simulations. Three-stage scheme of work was assumed in the project. First, the quality of the model identification was assessed for various combinations of pressure and flow signals recorded during the interruption. Then, the correlation between the working characteristics of the interrupter valve and the precision of the parameter estimation were assessed for the TD-EIT algorithm. Finally, a verification experiment by forward-inverse modeling was organized, in which the mechanical characteristics of a complex model were mapped with reduced analogs and with the use of neural networks for three typical modes: 'Normal state', 'Airway constriction' and 'Cheeks supported'. Obtained results show that to became effective in time-domain post-interrupter data exploration, both pressure and flow signals should be used in assessment of respiratory mechanics, taken in a range of at least 100ms and when both slopes (valve closing and opening) of quasi-step excitation are included. What is more, the faster the valve the smaller error of parameter estimation in proposed TD-EIT was observed, and this uncertainty importantly falls down for the length of time window exceeding the limit of 100ms. The pioneering use of neural network for mapping the mechanical properties of lungs with the use of interrupter experiment methodology proves that it is possible to conclude about more (than one) number of parameters characterizing the complex system and that this insight is biased with the error not exceeding of 10%; only peripheral properties are estimated worse. Such observation has a potential to change the experimental protocol, which was used in interrupter measurements up to date and to make this technique more attractive in comparison to other method, i.e. forced oscillation technique or impulse oscillometry. As regards the practical meaning of reported results for engineers and end-users (physicians and patients), proposed solution can be applied in simple portable devices with a feature of easy operation (important for e-monitoring).

Resumo Limpo

paper answer question possibl conclud object way one rint classic number paramet time domain postinterrupt signal occlusion measur respiratori mechan also verifi accuraci can achiev attempt obtain report result timedomain enhanc interrupt techniqu tdeit develop paper use comput simul threestag scheme work assum project first qualiti model identif assess various combin pressur flow signal record interrupt correl work characterist interrupt valv precis paramet estim assess tdeit algorithm final verif experi forwardinvers model organ mechan characterist complex model map reduc analog use neural network three typic mode normal state airway constrict cheek support obtain result show becam effect timedomain postinterrupt data explor pressur flow signal use assess respiratori mechan taken rang least ms slope valv close open quasistep excit includ faster valv smaller error paramet estim propos tdeit observ uncertainti import fall length time window exceed limit ms pioneer use neural network map mechan properti lung use interrupt experi methodolog prove possibl conclud one number paramet character complex system insight bias error exceed peripher properti estim wors observ potenti chang experiment protocol use interrupt measur date make techniqu attract comparison method ie forc oscil techniqu impuls oscillometri regard practic mean report result engin endus physician patient propos solut can appli simpl portabl devic featur easi oper import emonitor

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