Lütkenhöner Bernd
ENT Clinic, Münster University Hospital, Kardinal-von-Galen-Ring 10, Münster, Germany.
Theor Biol Med Model. 2015 Oct 6;12:21. doi: 10.1186/s12976-015-0018-x.
The vestibular evoked myogenic potential (VEMP) can be modelled reasonably well by convolving two functions: one representing an average motor unit action potential (MUAP), the other representing the temporal modulation of the MUAP rate (rate modulation). It is the latter which contains the information of interest, and so it would be desirable to be able to estimate this function from a combination of the VEMP with some other data. As the VEMP is simply a stimulus-triggered average of the electromyogram (EMG), a supplementary, easily accessible source of information is the EMG power spectrum, which can be shown to be roughly proportional to the squared modulus of the Fourier transform of the MUAP. But no phase information is available for the MUAP so that a straightforward deconvolution is not possible.
To get around the problem of incomplete information, the rate modulation is described by a thoughtfully chosen function with just a few adjustable parameters. The convolution model is then used to make predictions as to the energy spectral density of the VEMP, and the parameters are optimized using a cost function that quantifies the difference between model prediction and data.
The workability of the proposed approach is demonstrated by analysing Monte Carlo simulated data and exemplary data from patients who underwent VEMP testing as part of a clinical evaluation of their dizziness symptoms.
The approach is suited, for example, to estimate the duration of the inhibition causing the VEMP or to disentangle a VEMP consisting of more than one component.
通过对两个函数进行卷积，可以很好地模拟前庭诱发肌源性电位（VEMP）：一个函数代表平均运动单位动作电位（MUAP），另一个函数代表MUAP发放率的时间调制（发放率调制）。正是后者包含了感兴趣的信息，因此希望能够从VEMP与其他一些数据的组合中估计该函数。由于VEMP只是肌电图（EMG）的刺激触发平均值，一个补充的、易于获取的信息源是EMG功率谱，它可以证明大致与MUAP的傅里叶变换的平方模成正比。但是对于MUAP没有相位信息，因此无法进行直接的反卷积。
为了解决信息不完整的问题，发放率调制由一个精心选择的、只有几个可调参数的函数来描述。然后使用卷积模型对VEMP的能量谱密度进行预测，并使用一个量化模型预测与数据之间差异的代价函数来优化参数。
通过分析蒙特卡罗模拟数据和作为头晕症状临床评估一部分接受VEMP测试的患者的示例数据，证明了所提出方法的可行性。
例如，该方法适用于估计引起VEMP的抑制持续时间或解析由多个成分组成的VEMP。
