Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors
Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors
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DOI：
10.1109/tbme.2020.3034632
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发表时间：
2021-05-01
影响因子：
4.6
通讯作者：
Faghih, Rose T.
中科院分区：
文献类型：
--
作者：
Amin, Md Rafiul;Faghih, Rose T.
目的：汗液分泌导致皮肤电导信号的变化。SC的相对快速的变化，称为相位分量，反映了交感神经系统的活动。与体温调节和一般唤醒有关的缓慢变化被称为紧张成分。将SC信号分解成其成分以破译与情绪唤醒相关的编码神经信息是具有挑战性的。方法：我们使用二阶微分方程模拟出汗的扩散和蒸发过程的相位分量。我们包括一个稀疏的脉冲神经信号，刺激汗腺产生汗液。我们用几个三次B样条函数来模拟主音分量。我们制定了一个优化问题的生理先验的系统参数，稀疏性之前的神经刺激，和一个平滑之前的紧张成分。最后，我们采用了广义交叉验证为基础的坐标下降的方法，以平衡之间的平稳性的主音分量，稀疏的神经刺激，和残留。结果如下：我们说明，我们可以成功地恢复未知分离紧张性和相位成分从实验和模拟数据（R-2 > 0.95）。此外，我们成功地证明了我们的能力，自动识别稀疏水平的神经刺激和平滑水平的主音成分。结论：我们的广义交叉验证为基础的新方法SC信号分解成功地解决了以前的挑战，并检索生理上合理的解决方案。意义：SC的准确分解可以潜在地改善精神障碍患者的认知应激追踪。
Objective: Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal. Methods: We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual. Results: We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with R-2 > 0.95). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component. Conclusion: Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution. Significance: Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.