Characterization of an Algorithm for Autonomous, Closed-Loop Neuromodulation During Motor Rehabilitation

Recent evidence demonstrates that manually triggered vagus nerve stimulation (VNS) combined with rehabilitation leads to increased recovery of upper limb motor function after stroke. This approach is premised on studies demonstrating that the timing of stimulation relative to movements is a key determinant in the effectiveness of this approach.

The overall goal of the study was to identify an algorithm that could be used to automatically trigger VNS on the best movements during rehabilitative exercises while maintaining a desired interval between stimulations to reduce the burden of manual stimulation triggering.

To develop the algorithm, we analyzed movement data collected from patients with a history of neurological injury. We applied 3 different algorithms to the signal, analyzed their triggering choices, and then validated the best algorithm by comparing triggering choices to those selected by a therapist delivering VNS therapy.

The dynamic algorithm triggered above the 95th percentile of maximum movement at a rate of 5.09 (interquartile range [IQR] = 0.74) triggers per minute. The periodic algorithm produces stimulation at set intervals but low movement selectivity (34.05%, IQR = 7.47), while the static threshold algorithm produces long interstimulus intervals (27.16 ± 2.01 seconds) with selectivity of 64.49% (IQR = 25.38). On average, the dynamic algorithm selects movements that are 54 ± 3% larger than therapist-selected movements.

This study shows that a dynamic algorithm is an effective strategy to trigger VNS during the best movements at a reliable triggering rate.