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AssigneeSociedade Beneficente Israelita Brasileira Hospital Albert Einstein
InventorFelipe BRUNETTO TANCREDI
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The present invention concerns a system and method of intelligent control, based on machine learning, to modulate end-tidal concentration levels through continuous adjustments in the volume and concentration of a flow of incoming respiratory gases (Gin) comprising the stages of: A) continuously sampling and measuring a first concentration and pressure signal (CP10) of the incoming gas flow (Gin) administered in a respiration device (200) at a current respiration moment (T0); b) continuously sampling and measuring a second concentration and pressure signal (CP20) of a flow of respiration gases (GRESP) within the breathing device (200) in the current respiration moment (T0); c) estimating, based on the signals sampled and measured in steps a) and b) and in the concentration and pressure signals (CP1n, CP2n) measured in previous breathing moments (T-N), a new volume and a new concentration of oxygen and carbonic gas for the flow of incoming gases (Gin) for an immediate future inspiratory moment (T1); and d) adjusting the volume and concentration of oxygen and carbonic gas of the flow of incoming respiratory gases (Gin) within the same inspiratory cycle.
Intelligent control system, based on machine learning, to modulate end-tidal concentration levels through continuous adjustments in the volume and concentration of a flow of incoming respiratory gases (Gin) characterized by the fact that it is configured to sample and measure, in real time, a first of concentration and pressure signal (CP10) from the incoming gas flow (Gin) administered in a respiratory device (200) at a current breathing moment (T0 and a second concentration and pressure signal (CP20) of a respiration gas flow (GRESP) within the respiratory device (200) at the current respiratory moment (T0, the system being configured to estimate, in real time, and based on the first and second concentration and pressure signals (CP10, CP20) measured in the current moment of respiration (T0 and in the concentration and pressure signals (CP1n, CP2n) measured in prior respiratory moments (T-N), a new volume and a new concentration of oxygen and carbonic gas for the flow of incoming gases (Gin) for an immediate future inspiratory moment (Ti), the immediate future inspiratory moment (T1) occurring after the current respiratory moment (T0), the system being configured to adjust the volume and concentration of oxygen and carbonic gas of the flow of incoming gases (Gin) several times within the same inspiratory cycle.
Intelligent control method, based on machine learning, to modulate end-tidal concentration levels through continuous adjustments in the volume and concentration of a flow of incoming respiratory gases (Gin) characterized by fact that it comprises the stages of:
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