A comparative analysis of computational models for respiratory frequency

Abstract

Effective mechanical ventilation depends on precise control of multiple parameters, including respiratory frequency. This study explores the influence of respiratory frequency on ventilation through a comparative analysis of four mathematical models. Understanding optimal frequency selection is paramount for ventilator design and control algorithms. Furthermore, the relationship between frequency, minute ventilation, lung resistance, and elastance can aid in managing respiratory diseases. With this approach, the respiratory frequency can be optimized during assisted ventilation, contributing to a better understanding and control of various respiratory therapies. The models were evaluated by varying key physiological parameters such as resistance, elastance, and alveolar ventilation. The effects of parameter variations on predicted respiratory frequencies were illustrated graphically, accompanied by a sensitivity analysis to quantify how changes in parameters influence frequency. To further evaluate model performance, a comparison with published datasets was conducted. This comprehensive assessment ultimately identified a specific model that exhibited the least mean percentage error and closely resembled published data, highlighting its potential for future research and clinical applications.