Development of closed-loop modelling framework for adaptive respiratory pacemakers.

Objective: Ventilatory pacing by electrical stimulation of the phrenic nerve has many advantages compared to mechanical ventilation. However, commercially available respiratory pacing devices operate in an open-loop fashion, which require manual adjustment of stimulation parameters for a given patient. Here, we report the model development of a closed-loop respiratory pacemaker, which can automatically adapt to various pathological ventilation conditions and metabolic demands.

Methods: To assist the model design, we have personalized a computational lung model, which incorporates the mechanics of ventilation and gas exchange. The model can respond to the device stimulation where the gas exchange model provides biofeedback signals to the device. We use a pacing device model with a proportional integral (PI) controller to illustrate our approach.

Results: The closed-loop adaptive pacing model can provide superior treatment compared to open-loop operation. The adaptive pacing stimuli can maintain physiological oxygen levels in the blood under various simulated breathing disorders and metabolic demands.

Conclusion: We demonstrate that the respiratory pacing devices with the biofeedback can adapt to individual needs, while the lung model can be used to validate and parametrize the device.

Significance: The closed-loop model-based framework paves the way towards an individualized and autonomous respiratory pacing device development.