Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference.
Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference.
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皮肤电活动的生理特征使可扩展的近乎实时的自主神经系统激活推断成为可能。
DOI：
10.1371/journal.pcbi.1010275
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发表时间：
2022-07
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
皮肤电活动（EDA）是在皮肤上观察到的任何电现象。皮肤电导（SC）是EDA的量度，其显示由于自主神经系统（ANS）激活引起的汗液分泌的波动。由于它可以捕获心理生理信息，因此使用EDA跟踪心理和生理健康的研究工作显着增加。然而，目前的最先进的缺乏生理动机的方法，从EDA的ANS激活的实时推断。因此，首先，我们提出了一个全面的供应链动态模型。所提出的模型是一个三维状态空间表示的直接分泌的汗水通过毛孔开放和扩散，然后相应的蒸发和重吸收。作为模型的输入，我们考虑表示导致汗腺产生汗液的ANS激活的稀疏信号。其次，我们推导出一个可扩展的固定间隔平滑的稀疏恢复方法，利用所提出的综合模型来推断ANS激活，使边缘计算。我们采用了广义交叉验证来调整稀疏度。最后，我们提出了一种基于期望最大化的反卷积方法，用于在ANS激活推理过程中学习模型参数。为了进行评估，我们利用了一个有26个参与者的数据集，结果表明，我们的综合状态空间模型可以成功地描述SC的变化，具有高度的可扩展性，显示了实时应用的可行性。结果验证了我们的生理动机的状态空间模型可以全面地解释EDA和优于所有以前的方法。我们的研究结果引入了一个全新的视角，并对EDA分析的标准实践产生了更广泛的影响。目前的最先进的缺乏生理学动机的模型，皮肤电活动（EDA），有能力全面描述的变化，皮肤电导（SC）-EDA的措施。在这项研究中，我们提出了一个生理动机的状态空间模型，以解决以前的挑战。另一方面，还缺乏同时求解生理系统参数的可扩展自主神经系统（ANS）激活推断方法。此外，我们开发了一个可扩展的ANS激活推理方法的基础上提出的模型与实时边缘计算的目标。我们利用一个有26个参与者的数据集来验证新模型和可扩展的方法。结果表明，我们的生理动机的状态空间模型可以全面解释EDA。我们的研究结果引入了一个全新的视角，并对EDA分析的标准实践产生了更广泛的影响。
Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis. The current state-of-the-art lacks physiology-motivated models for electrodermal activities (EDA) that have the power to comprehensively describe the variations in skin conductance (SC)–a measure of EDA. In this study, we propose a physiology-motivated state-space model to address previous challenges. On the other hand, there is also an absence of a scalable autonomic nervous system (ANS) activation inference method that simultaneously solve for the physiological system parameters. Furthermore, we develop a scalable ANS activation inference approach based on the proposed model with a goal for real-time edge computation. We utilize a dataset with 26 participants to validate the new model and the scalable method. Results demonstrate that our physiology-motivated state-space model can comprehensively explain the EDA. Our findings introduce a whole new perspective and have a broader impact on standard practices of EDA analysis.
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影响因子：
3
作者：
Bach DR;Flandin G;Friston KJ;Dolan RJ
通讯作者：
Dolan RJ
影响因子：
3.7
作者：
Faghih RT;Dahleh MA;Adler GK;Klerman EB;Brown EN
通讯作者：
Brown EN
影响因子：
2.6
作者：
Bach, Dominik R.;Friston, Karl J.;Dolan, Raymond J.
通讯作者：
Dolan, Raymond J.
影响因子：
4.3
作者：
Faghih RT;Dahleh MA;Brown EN
通讯作者：
Brown EN
影响因子：
3
作者：
Bach, Dominik R.;Flandin, Guillaume;Friston, Karl J.;Dolan, Raymond J.
通讯作者：
Dolan, Raymond J.