Abstract: In accordance with the present disclosure, a system and method of intelligent control is provided that is 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. The system and the method are able to continually estimate and adjust new gas flows that are administered for a user to inhale in immediate future moments of inspiration, based on the concentration and pressure signals collected at the actual moment of breathing and at previous moments of breathing, without the need for reservoirs to store inspired or expired gases, or for aprioristic physiological models.

Abstract: 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) adj