Online Bayesian optimization of vagus nerve stimulation

Elvijs Sarkans, Antonin Berthon

Abstract:

Objective. In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain
or organs, modifying their function. Stimulation devices capable of triggering exogenous neural
signals using electrical waveforms require a complex and multi-dimensional parameter space to
control such waveforms. Determining the best combination of parameters (waveform optimization
or dosing) for treating a particular patient’s illness is therefore challenging. Comprehensive
parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to
the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic
results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter
search, we present a flexible machine learning, data acquisition, and processing framework for
optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian
optimization. This optimization builds a model of the neural and physiological responses to
stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy
of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral
organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating
the effectiveness of our approach. Main results. The efficacy of our optimization approach was first
evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in
porcine subjects. Optimization converged quickly on parameters achieving target HRs and
optimizing neural B-fiber activations despite high intersubject variability. Significance. An
optimized stimulation waveform was achieved in real time with far fewer stimulations than
required by alternative optimization strategies, thus minimizing exposure to side effects.
Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that
a complex set of neural stimulation parameters can be optimized in real-time for a patient to
achieve a personalized precision dosing.