Texas Biomedical Device Center, The University of Texas at Dallas, Richardson, TX, USA.
Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX, USA.
Neurorehabil Neural Repair. 2024 Jul;38(7):493-505. doi: 10.1177/15459683241252599. Epub 2024 May 7.
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.
最近的证据表明，手动触发迷走神经刺激（VNS）结合康复可以促进中风后上肢运动功能的恢复。这种方法的前提是研究表明，刺激与运动的时间关系是这种方法有效性的关键决定因素。
该研究的总体目标是确定一种算法，该算法可以在康复运动过程中自动触发 VNS 最佳运动，同时保持刺激之间的期望间隔，以减少手动刺激触发的负担。
为了开发算法，我们分析了来自有神经损伤病史的患者的运动数据。我们将 3 种不同的算法应用于信号，分析它们的触发选择，然后通过将触发选择与治疗师提供 VNS 治疗时的选择进行比较，验证最佳算法。
动态算法以 5.09 次/分钟（四分位距[IQR] = 0.74）的速度在最大运动的第 95 个百分位数以上触发。周期性算法以设定的间隔产生刺激，但运动选择性低（34.05%，IQR = 7.47），而静态阈值算法产生的刺激间隔较长（27.16 ± 2.01 秒），选择性为 64.49%（IQR = 25.38）。平均而言，动态算法选择的运动比治疗师选择的运动大 54 ± 3%。
这项研究表明，动态算法是一种在可靠触发率下触发 VNS 最佳运动的有效策略。
