Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation

Hurtado, Daniel E; Chávez, Javier A P; Mansilla, Roberto; Lopez, Roberto; Abusleme, Angel

doi:10.1109/ACCESS.2020.3045603

All Outputs (1)

Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation (2020)
Journal Article
Hurtado, D. E., Chávez, J. A. P., Mansilla, R., Lopez, R., & Abusleme, A. (2020). Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation. IEEE Access, 8, 227936-227944. https://doi.org/10.1109/ACCESS.2020.3045603

Continuous monitoring of ventilatory parameters such as tidal volume (TV) and minute ventilation (MV) has shown to be effective in the prevention of respiratory compromise events in hospitalized patients. However, the non-invasive estimation of respi... Read More about Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation.