A novel controller based on state-transition models for closed-loop vagus nerve stimulation: Application to heart rate regulation.

Kristen Sparrow • January 16, 2020

Complicated system of feedback for implanted vagus nerve stimulators.  Posted for the record.

2017 Oct 27;12(10):e0186068. doi: 10.1371/journal.pone.0186068. eCollection 2017.

A novel controller based on state-transition models for closed-loop vagus nerve stimulation: Application to heart rate regulation.

Author information

INSERM, U1099, Rennes, F-35000, France.
Université de Rennes 1, LTSI, Rennes, F-35000, France.
Sorin CRM SAS (a LivaNova company), Clamart, France.
INSERM, UMR970 Paris Cardio-vascular Research Center, Paris, France, Assistance Publique-Hôpitaux de Paris, Department of Cardiology, Hôpital Européen Georges Pompidou, Paris, France, Paris Descartes University, PRES Paris Sorbonne, Paris, France.
CHU Rennes, Department of Cardiology and INSERM, CIC-IT 1414, Rennes, F-35000, France.


Vagus nerve stimulation (VNS) is an established adjunctive therapy for pharmacologically refractory epilepsy and depression and is currently in active clinical research for other applications. In current clinical studies, VNS is delivered in an open-loop approach, where VNS parameters are defined during a manual titration phase. However, the physiological response to a given VNS configuration shows significant inter and intra-patient variability and may significantly evolve through time. VNS closed-loop approaches, allowing for the optimization of the therapy in an adaptive manner, may be necessary to improve efficacy while reducing side effects. This paper proposes a generic, closed-loop control VNS system that is able to optimize a number of VNS parameters in an adaptive fashion, in order to keep a control variable within a specified range. Although the proposed control method is completely generic, an example application using the cardiac beat to beat interval (RR) as control variable will be developed in this paper. The proposed controller is based on a state transition model (STM) that can be configured using a partially or a fully-connected architecture, different model orders and different state-transition algorithms. The controller is applied to the adaptive regulation of heart rate and evaluated on 6 sheep, for 13 different targets, using partially-connected STM with 10 states. Also, partially and fully-connected STM defined by 30 states were applied to 7 other sheep for the same 10 targets. Results illustrate the interest of the proposed fully-connected STM and the feasibility of integrating this control system into an implantable neuromodulator.