Covidien LP

Kun Li

Gabriel Sanchez

Periagounder R Arul

Nancy F Dong

Fengdan Dong

Systems and methods for model-driven system integration on a ventilator comprise a modeled exhalation flow. Ventilator flow sensors contain components that are easily damaged or impacted and have high rates of failure. Knowledge failure frequency and type may help determine when to replace, clean, or calibrate a flow sensor. A modeled exhalation flow may be trained for a ventilator. Additionally, the modeled exhalation flow may be compared to a flow sensor flow to determine a specific failure. Additionally or alternatively, the modeled exhalation flow may supplement, weight, or replace a faulty flow sensor measurement. These systems and methods may assist in identifying an inaccurate flow sensor measurement and/or providing a more accurate modeled flow estimate, thus increasing accuracy in patient-ventilator interactions.

Model-driven system integration in medical ventilators

Systems and methods for novel ventilation that allows the patient to trigger or initiate the delivery of a breath are provided. Further, systems and methods for triggering ventilation based on signal distortion of a monitored patient parameter are provided.

Systems and methods for respiratory effort detection utilizing signal distortion

This disclosure describes systems and methods for controlling an exhalation valve based on pressure and/or flow measurements during exhalation. The disclosure describes novel exhalation valve controls for ventilating a patient.

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San Diego, CA, United States of America

Kun Li