Michael L McKinnon, N Jeremy Hill, Jonathan S Carp, Blair Dellenbach, Aiko K Thompson
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The current study automates such judgments.<i>Approach.</i>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<i>H</i><sub>max</sub>. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.<i>Main results.</i>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 elicit<i>H</i><sub>max</sub>fell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.<i>Significance.</i>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.</p>","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445400/pdf/","citationCount":"0","resultStr":"{\"title\":\"Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.\",\"authors\":\"Michael L McKinnon, N Jeremy Hill, Jonathan S Carp, Blair Dellenbach, Aiko K Thompson\",\"doi\":\"10.1088/1741-2552/ace6fb\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Objective.</i>Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. 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引用次数: 0
摘要
霍夫曼(H-)反射的表面肌电图测量在广泛的神经科学和临床应用中至关重要。H反射操作性条件反射是一种很有前景的新兴治疗应用,通过这种训练,人可以调节H反射,从而对慢性神经肌肉疾病患者的感觉运动功能产生普遍的有益影响。传统的诊断和新型的实时治疗应用都依赖于对 H 反射和 M 波时间界限的准确定义,而目前这依赖于专家的个案判断。我们基于小波的新型算法可自动确定人体比目鱼肌 H反射和M波的时间范围和振幅。在 20 名参与者中,每个人都根据 3 或 4 分钟招募曲线的初步测量数据对算法进行了训练。输出结果根据后续训练的募集曲线(所有参与者共 92 条曲线)的参数拟合结果,以及在接近 Hmax 时进行的调节方案后续基线试验(每位参与者 1200 次)进行评估。主要结果:自动界限与人工估计一致:95%在±2.5 毫秒以内。由此得出的 H 反射幅度估计值显示,在哪些试验可被视为成功的操作性条件反射方面,自动界值与回顾界值之间的一致性非常好(参与者平均值为 97.5%)。自动方法和人工方法之间的招募曲线参数也非常一致:95% 的自动估计值在±1.4% 的回溯估计值范围内;对于产生最大值 10% 的 M 波的 "阈值 "电流,该值为±3.5%。
Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications.
Objective.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.Approach.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 nearHmax. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Main results.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 elicitHmaxfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Significance.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.
期刊介绍:
The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels.
The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.