Mechanical ventilation control has gained increasing attention in the last few decades, especially within Intensive Care Units, for the treatment of patient with respiratory issues or in the post-operative phase. In this work, we review a simple model of physiological ventilation and define a ventilation control problem for the case of assisted ventilation, where an individual exhibits an own respiratory drift which is, anyway, insufficient to guarantee safe breathing conditions. Frequency and phase of the patient's respiratory signal are assumed to be unknown by the ventilator and are estimated by means of a frequency estimator algorithm (based on a two-stage autocorrelation approach) and with a Phase Locked Loop (PLL), respectively. The control algorithm, at each iteration, varies the amplitude of the pressure wave at the mouth, while the waveform phase and frequency are chosen based on the current estimates of the corresponding quantities in the spontaneous breathing. In some preliminary numerical simulations, a reference tidal volume is correctly tracked, showing the potential of the approach taken.

Assisted Ventilation Control Based on Phase and Frequency Estimation of Respiratory Drift

D'Orsi Laura;Borri Alessandro;De Gaetano Andrea
2019

Abstract

Mechanical ventilation control has gained increasing attention in the last few decades, especially within Intensive Care Units, for the treatment of patient with respiratory issues or in the post-operative phase. In this work, we review a simple model of physiological ventilation and define a ventilation control problem for the case of assisted ventilation, where an individual exhibits an own respiratory drift which is, anyway, insufficient to guarantee safe breathing conditions. Frequency and phase of the patient's respiratory signal are assumed to be unknown by the ventilator and are estimated by means of a frequency estimator algorithm (based on a two-stage autocorrelation approach) and with a Phase Locked Loop (PLL), respectively. The control algorithm, at each iteration, varies the amplitude of the pressure wave at the mouth, while the waveform phase and frequency are chosen based on the current estimates of the corresponding quantities in the spontaneous breathing. In some preliminary numerical simulations, a reference tidal volume is correctly tracked, showing the potential of the approach taken.
2019
Istituto di Analisi dei Sistemi ed Informatica ''Antonio Ruberti'' - IASI
Inglese
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
879
885
Sì, ma tipo non specificato
06-09/10/2019
Bari, Italy
Ventilation
Frequency estimation;Lung;Mathematical model;Correlation;Frequency control;Mouth;artificial assisted ventilation;mathematical modeling;control applications;parameters setting estimation
4
none
D'Orsi, Laura; Mameli, Marco; Borri, Alessandro; DE GAETANO, Andrea
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/373703
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