A Neuro-Fuzzy Controller for the estimation of Tidal Volume and Respiration Frequency ventilator settings for COPD patients ventilated in control mode

Patients with chronic obstructive pulmonary disease (COPD) are characterized by increased work of breathing (WOB) and ventilatory muscle dysfunction. Mechanical ventilation is applied to unload the WOB; rest respiratory muscles decrease arterial partial pressure of carbon dioxide (PaCO₂) and treat hypoxemia. Since patients' needs are not static, ventilator settings have to be adjusted regularly. The aim of the present study was the development and evaluation of a neuro-fuzzy controller, that utilizes non- invasively acquired parameters for the determination of the appropriate tidal volume (VT) and respiration frequency (RR) ventilator settings for COPD patients. Forty three (43) hours of non-invasively monitored physiology parameters and ventilator settings, from four (4) different COPD patients ventilated in control mode, were collected in two (2) General Hospitals in Greece. Recorded data were randomly allocated into two sets, namely training set (60%) and evaluation set (40%). A neuro- fuzzy controller was developed and trained, by employing the training set. The controller utilizes non-invasively measured parameters, namely oxygen saturation (SpO₂), lung compliance (C) and resistance (R), Peak Inspiratory pressure (PIP) and Plateau pressure (P_plateau), for predicting appropriate VT and RR settings. The developed neuro-fuzzy controller was tested against evaluation set. The Mean Square Error of the tidal volume and the respiration rate was 0.222 ml/Kgr and 1.21 breaths per minute (bpm) respectively.