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用于在诊断和闭环治疗应用中外周神经刺激诱发的肌电图反应的自动描绘和评估的方法。

Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.

机构信息

BioCircuit Technologies Inc., Atlanta, GA, United States of America.

National Center for Adaptive Neurotechnologies, Stratton VA Medical Center, Albany, NY, United States of America.

出版信息

J Neural Eng. 2023 Jul 21;20(4). doi: 10.1088/1741-2552/ace6fb.

Abstract

Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions' recruitment curves (92 curves across all participants) and on the conditioning protocol's subsequent baseline trials (∼1200 per participant) performed near. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicitfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.

摘要

表面肌电图测量霍夫曼(H-)反射在广泛的神经科学和临床应用中至关重要。一种有前途的新兴治疗应用是 H 反射操作性条件反射,通过这种反射,人们可以接受训练来调节 H 反射,从而对慢性神经肌肉疾病中的感觉运动功能产生普遍的有益影响。传统的诊断和新型实时治疗应用都依赖于 H 反射和 M 波时间边界的准确定义,而这些定义目前依赖于专家的逐案判断。本研究实现了这种判断的自动化。我们的新小波算法自动确定了人类比目鱼肌 H 反射和 M 波的时间范围和幅度。在 20 名参与者中的每一名中,算法都在初步 3 或 4 分钟募集曲线测量中的数据上进行了训练。输出结果在随后的募集曲线的参数拟合(所有参与者共 92 个曲线)和接近时的条件反射协议的随后的基线试验(每个参与者约 1200 个)上进行了评估。结果与当时估计的原始时间边界以及 6 年后由专家进行的回顾性估计进行了比较。自动边界与手动估计吻合良好:95%的边界在±2.5 毫秒以内。自动和回顾性边界之间的 H 反射幅度估计结果在哪些试验将被认为适合作操作性条件反射方面具有极好的一致性(参与者平均为 97.5%)。募集曲线参数也与自动和手动方法吻合良好:自动方法中诱发所需电流的 95%估计值在回顾性估计值的±1.4%以内;对于产生 10%最大 M 波的“阈值”电流,该值在±3.5%以内。这种对 M 波和 H 反射定义的可靠自动化应该使既定和新兴的 H 反射方案大大减少人员间变异性和人为错误的影响,从而增加转化的潜力。

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