Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors

Amin, Md. Rafiul; Faghih, Rose T.

doi:10.1109/tbme.2020.3034632

Cited by 28 publications

(52 citation statements)

References 49 publications

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“…In recent years, there has been a tremendous increase in the popularity of wearable devices, which has greatly increased the feasibility of non-invasive and continuous physiological data collection [1,2]. Electrodermal activity (EDA) is one example, which has been used as a non-invasive surrogate marker of the autonomous nervous system in several psychophysiological applications, such as emotional arousal [3][4][5][6], stress [7][8][9][10], pain [11][12][13], panic disorder [14], autism [15], and decision making [16]. EDA refers to the change in electrical conductivity of the skin in response to eccrine sweat gland activity.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices’ Quality and Robustness against Motion Artifact

Hossain

Kong

Posada-Quintero

et al. 2022

Sensors

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The most traditional sites for electrodermal activity (EDA) data collection, palmar locations such as fingers or palms, are not usually recommended for ambulatory monitoring given that subjects have to use their hands regularly during their daily activities, and therefore, alternative sites are often sought for EDA data collection. In this study, we collected EDA signals (n = 23 subjects, 19 male) from four measurement sites (forehead, back of neck, finger, and inner edge of foot) during cognitive stress and induction of mild motion artifacts by walking and one-handed weightlifting. Furthermore, we computed several EDA indices from the EDA signals obtained from different sites and evaluated their efficiency to classify cognitive stress from the baseline state. We found a high within-subject correlation between the EDA signals obtained from the finger and the feet. Consistently high correlation was also found between the finger and the foot EDA in both the phasic and tonic components. Statistically significant differences were obtained between the baseline and cognitive stress stage only for the EDA indices computed from the finger and the foot EDA. Moreover, the receiver operating characteristic curve for cognitive stress detection showed a higher area-under-the-curve for the EDA indices computed from the finger and foot EDA. We also evaluated the robustness of the different body sites against motion artifacts and found that the foot EDA location was the best alternative to other sites.

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Section: Introductionmentioning

confidence: 99%

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices’ Quality and Robustness against Motion Artifact

Hossain

Kong

Posada-Quintero

et al. 2022

Sensors

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“…Therefore proposed optimization problem might suffer from overfitting as pointed out in ( 41 ). As a future work of this, appropriate probabilistic priors can be applied on the system parameters to prevent such overfitting ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

Sparse System Identification of Leptin Dynamics in Women With Obesity

et al. 2022

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The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity.

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“…In a similar time, Bach et al [20] reported that bi-exponential functions provided better fit than other candidates while modeling the fast varying component after removing the slow varying component with low-pass filter. Nevertheless, the FIR filter-based separation of the slow and fast varying components has limitations as pointed out in our previous work [24].…”

Section: Introductionmentioning

confidence: 95%

“…They assumed that SC is single-phasic and, more specifically, that all fluctuations can be defined with the second-order differential equation. However, eventually researchers have realized the bi-phasic nature of EDA fluctuations, meaning there are two different components in EDA that vary in two different rates [19][20][21][22][23][24]. Bach et al [25] have used a low-pass filter to separate slow varying component and then investigated the fast varying component as the output of a finite linear time-invariant (LTI) filter.…”

Section: Introductionmentioning

confidence: 99%

Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference

Amin

Faghih

2022

PLoS Comput Biol

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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.

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Identification of Sympathetic Nervous System Activation From Skin Conductance: A Sparse Decomposition Approach With Physiological Priors

Cited by 28 publications

References 49 publications

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices’ Quality and Robustness against Motion Artifact

Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices’ Quality and Robustness against Motion Artifact

Sparse System Identification of Leptin Dynamics in Women With Obesity

Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference

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