Irregular activation of individual sweat glands in human sole observed by a videomicroscopy

Nishiyama, Tomoki; Sugenoya, Junichi; Matsumoto, Takaaki; Iwase, Satoshi; Mano, Tadaaki

doi:10.1016/s1566-0702(01)00229-6

Cited by 49 publications

(53 citation statements)

References 22 publications

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“…This could be due to a reduction in SC as the skin is hydrated, leading to swelling of the stratum corneum and sweat pore closure, an effect explained in Edelberg. 10 It is also known that evaporation follows the electrical response by roughly 5 s. 16,22 The last factor to consider is the responsiveness of the QSweat device used to measure Sw in this study, which may have provided slow recordings despite the backwards filtering correction and due to the necessary aggressive low-pass filtering.…”

Section: Discussionmentioning

confidence: 99%

Improved Estimation of Sweating Based on Electrical Properties of Skin

et al. 2013

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Skin conductance (SC) has previously been reported to correlate strongly with sweat rate (Swr) within subjects, but weakly between subjects. Using a new solution for simultaneous recording of SC, skin susceptance (SS) and skin potential (SP) at the same skin site, the aim of this study was to assess how accurately sweat production can be estimated based on combining these electrical properties of skin. In 40 subjects, SC, SS, SP and Swr by skin water loss was measured during relaxation and mental stress. SC and Swr had high intraindividual correlations (median r = 0.77). Stepwise multilinear regression with bootstrap validation lead to a sweating estimation model based on the sum of SC increases, the SP area under the curve and the SS area under the curve, yielding an interindividual accuracy of R(2) = 0.73, rmse = 12.9%, limits of agreement of +27.6, -30.4% and an intraclass correlation coefficient of 0.84. Bootstrapping of training and test-sets gave median rmse = 15.4%, median R(2) = 0.66. The model was also validated for intraindividual variability. The results show that estimation of sweating is significantly improved by the addition of SS and SP measurement.

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Section: Discussionmentioning

confidence: 99%

Improved Estimation of Sweating Based on Electrical Properties of Skin

et al. 2013

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“…These bursts are temporally discrete episodes [19], [20], i.e. SCRs are generated by a neural signal that is sparse and non-negative because of the nature of a nerve activity.…”

Section: B Model Assumptionsmentioning

confidence: 99%

“…SCRs are generated by a neural signal that is sparse and non-negative because of the nature of a nerve activity. A2) The relationship between the number of sweat glands recruited and the amplitude of a firing burst is linear [20]. Moreover, the output response of the system depends only on the instant where the nerve input is applied.…”

Section: B Model Assumptionsmentioning

confidence: 99%

cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing

Greco

Valenza

Lanatà

et al. 2016

IEEE Trans. Biomed. Eng.

294

288

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Abstract-Goal: This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. EDA can be considered one of the most common observation channels of sympathetic nervous system activity, and manifests itself as a change in electrical properties of the skin, such as skin conductance (SC). Methods: The proposed model describes SC as the sum of three terms: the phasic component, the tonic component, and an additive white Gaussian noise term incorporating model prediction errors as well as measurement errors and artifacts. This model is physiologically inspired and fully explains EDA through a rigorous methodology based on Bayesian statistics, mathematical convex optimization and sparsity. Results: The algorithm was evaluated in three different experimental sessions to test its robustness to noise, its ability to separate and identify stimulus inputs, and its capability of properly describing the activity of the autonomic nervous system in response to strong affective stimulation. Significance: Results are very encouraging, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.

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“…By transforming the skin-conductance time-series into an approximation of the original episodes of bursting, we are able to separate otherwise overlapping SCRs. A number of physiological studies have shown that the SCR response is preceded by discrete bursts of the sudomotor nerves that control the sweat glands (Macefield and Wallin, 1996;Nishiyama et al, 2001). These bursts have a mean duration of 638 ms, much shorter than the duration of the SCR.…”

Section: Modelling Scr Activation Using a Biexponential Functionmentioning

confidence: 99%

“…In addition, the large bursts that produce an SCR are temporally discrete episodes, that is, they are separated by large intervals relative to the duration of the bursts themselves (see Macefield and Wallin, 1996, Fig. 1;Nishiyama et al, 2001, Fig. 1).…”

Section: Modelling Scr Activation Using a Biexponential Functionmentioning

confidence: 99%