ArticleNeurorehabilitation and neural repair2024
Characterization of an Algorithm for Autonomous, Closed-Loop Neuromodulation During Motor Rehabilitation.
Article in Neurorehabilitation and neural repair, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- At-Home Delivery of Vagus Nerve Stimulation Paired With Task-Specific Training Improves Performance of High-Priority Activities in Persons With Chronic Spinal Cord Injury or Stroke.American journal of physical medicine & rehabilitation · 2026Trial
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- Closed-loop movement-paired transcutaneous auricular vagus nerve stimulation for upper-limb rehabilitation: a feasibility study.Journal of neuroengineering and rehabilitation · 2026Article
- Prediction of cardiac cycle duration for cardiac-gated closed-loop auricular vagus nerve stimulation.Frontiers in neuroscience · 2026Article
- Clinical experience implanting a miniature externally powered vagus nerve stimulator.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Article
- Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.Brain sciences · 2024Article
- Low-Intensity Focused Ultrasound Neuromodulation for Stroke Recovery: A Novel Deep Brain Stimulation Approach for Neurorehabilitation?IEEE open journal of engineering in medicine and biology · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundRecent 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.
objectiveThe 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.
methodsTo 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.
resultsThe 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.
conclusionsThis study shows that a dynamic algorithm is an effective strategy to trigger VNS during the best movements at a reliable triggering rate.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.