A system for carbon dioxide (CO2) removal from a circulatory system of a patient includes a medical device providing extracorporeal lung assist (ECLA) treatment to the patient through extracorporeal removal of CO2 from the patient's blood; at least one control unit controlling the operation of the medical device so as to control a degree of CO2 removal obtained by the ECLA treatment; and a bioelectric sensor detecting a bioelectric signal indicative of the patient's efforts to breathe. The at least one control unit is configured to control the operation of the medical device based on the detected bioelectric signal.

A process and a device determine an indicator of an intrinsic end-expiratory pressure in the lungs of a patient. Embodiments are based on the device, ventilator with the device, and the process using the device that includes an interface arrangement configured for an exchange of information with a ventilation device and a control unit that determines first information on a first breathing pressure generated by muscles of the patient, at a first time, at which an inhalation attempt of the patient is present and determines second information on a second breathing pressure generated by the muscles of the patient, at a second time, at which breathing gas flow towards the patient starts. The control unit further determines the indicator of the intrinsic end-expiratory pressure based on the first information and based on the second information.

A ventilator system for a patient includes: an intubation tube configured to flow oxygen-enriched humidified air (OHA) toward patient lungs and to evacuate exhaust air exhaled from the lungs, the intubation tube includes: a distal end, configured to be inserted into patient trachea, and a proximal end, configured to be connected to tubes for receiving the OHA and evacuating the exhaust air; a first microgravity sensor, coupled to the intubation tube at a first position, and configured to produce a first signal indicative of a first micro-acceleration of the intubation tube at the first position; a second microgravity sensor, coupled to the intubation tube at a second different position, and configured to produce a second signal indicative of a second micro-acceleration of the intubation tube at the second position; and a processor, configured to control the ventilation system to apply a ventilation scheme responsively to the first and second signals.

The present invention discloses a carbon dioxide inhalation treatment device for central sleep apnea comprising a blower, a gas cylinder filled with carbon dioxide (CO.sub.2), an airbag, a mask, and a detection mechanism for detecting the central apnea by measuring the electromyographic activity of the chest wall muscles. The mask is provided with multiple holes providing a communication between the inside and outside of the mask in order to prevent any sense of resistance of breathing and to provide greater control of inspired CO.sub.2. Inspired CO.sub.2 from a gas mixture containing also a minimum 20% O.sub.2 is driven by air using a blower into a mixing chamber. This carbon dioxide inhalation treatment device for central sleep apnea can provide a stable, mild level of carbon dioxide for patients with central sleep apnea, thus by preserving respiratory drive correcting central sleep apnea without increasing the arousal and microarousal frequency.

A controller or processor(s) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus. The detection of such asynchrony events can then also serve as part of control logic for automated adjustments to the control parameters of the respiratory treatment generated by the respiratory treatment apparatus.

A ventilator system for a patient includes: an intubation tube configured to flow oxygen-enriched humidified air (OHA) toward patient lungs and to evacuate exhaust air exhaled from the lungs, the intubation tube includes: a distal end, configured to be inserted into patient trachea, and a proximal end, configured to be connected to tubes for receiving the OHA and evacuating the exhaust air; a first microgravity sensor, coupled to the intubation tube at a first position, and configured to produce a first signal indicative of a first micro-acceleration of the intubation tube at the first position; a second microgravity sensor, coupled to the intubation tube at a second different position, and configured to produce a second signal indicative of a second micro-acceleration of the intubation tube at the second position; and a processor, configured to control the ventilation system to apply a ventilation scheme responsively to the first and second signals.

A method, a computer program and a breathing apparatus relates to determination of at least one physiological parameter including the neuromechanical efficiency [NME] of a patient being mechanically ventilated by the breathing apparatus. This is achieved by obtaining samples of an airway pressure (P.sub.aw), a patient flow (Ø), a change in lung volume (V) caused by the patient flow, and an electrical activity of a respiratory muscle of the patient, during ventilation of the patient at a first level of ventilatory assist and a second and different level of ventilatory assist, and determining the at least one physiological parameter, including NME, from the airway pressure samples, the patient flow samples, the samples of the change in lung volume, and the samples of the electrical activity of the respiratory muscle, obtained at the different levels of ventilatory assist.

A ventilation machine is disclosed that includes a conduit interface configured to be connected to a respiratory system of a human or animal patient, an air flow generator configured to deliver air through the conduit interface into the respiratory system of the patient, a processing unit in communication with the air flow generator and configured to control the airflow generator to deliver air into the respiratory system of the patient according to a breathing scheme, and an induction device for activating a diaphragm of the patient. The induction device is in communication with the processing unit. The processing unit is configured to control the induction device to activate the diaphragm in coordination with the breathing scheme.

A mechanical ventilation system comprises a plurality of ventilation therapy sub-systems. Each of the ventilation therapy sub-systems is adapted to assist a respiratory function of the patient. The system also comprises a detector of the respiratory drive of the patient, an operator interface receiving one or more control parameters, and a main controller. The main controller assigns a therapeutic contribution to each of the ventilation therapy sub-systems based on the respiratory drive of the patient and on the control parameters. The controller modulates the respiratory drive of a patient by controlling each of the plurality of the ventilation therapy sub-systems according to its assigned therapeutic contribution. Distinct ventilation therapy sub-systems may apply negative pressure on the abdomen of the patient, deliver a non-pressurizing inspiratory flow to the patient, or induce a positive pressure in the airways of the patient.

A controller or processor(s) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus. The detection of such asynchrony events can then also serve as part of control logic for automated adjustments to the control parameters of the respiratory treatment generated by the respiratory treatment apparatus.