The measurement and analysis of electrodermal activity (EDA) offers applications in diverse areas ranging from market research to seizure detection and to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components that can obscure the signal information related to a user's response to a stimulus. We show how simple preprocessing followed by a novel compressed sensing based decomposition can mitigate the effects of the undesired noise components and help reveal the underlying physiological signal. The proposed framework allows for decomposition of EDA signals with provable bounds on the recovery of user responses. We test our procedure on both synthetic and real-world EDA signals from wearable sensors and demonstrate that our approach allows for more accurate recovery of user responses as compared with the existing techniques.
The authors of @cite_24 treat the SCL as a constant estimated by averaging the skin conductance signal over the time windows when the estimated SCR (by deconvolution) is below a certain amplitude. The work of @cite_30 presented a methodology to extract relevant SCR events while considering the SCL signal, but their matching pursuit-based technique used only a rough heuristic to remove this additional signal by deleting the two coarsest-scale components of a discrete-cosine transform applied to the skin conductance.
@cite_24 @cite_30
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Abstract Overlapping phasic skin conductance responses (SCRs) obtained using short interstimulus interval (ISI) paradigms such as those employed in cognitive research, confound measurement of each discrete phasic SCR as well as the tonic skin conductance level (SCL). We report a method of resolving this problem using a modelling technique that takes advantage of the stereotyped nature of the within-subject SCR waveform. A four-parameter sigmoid-exponential SCR model that describes the entire response, was developed and extended to five-, six- and eight-parameter skin conductance (SC) models. These SC models were successfully curve-fitted to more than 60 SC segments, each containing one SCR or two overlapping SCRs on a sloping baseline obtained from 20 normal subjects. The SC segments were consequently decomposed into their components: the tail of the previous response, one or two SCRs and the SCL. The SCRs free of the complication of overlap were then quantified. The raw SCRs of the same data set were also measured using a standard method. The standard measurement showed a significant reduction of 15 in amplitude and 140 ms in peak latency compared to our method. The basic four SCR model parameters—onset time, rise time, decay time constant and gain—showed increasing inter-subject variability in that order. These SCR model parameters may be studied as variables in normal and patient groups and as indices of treatment response. This quantitative method also provides a means to assess the relationships between central and autonomic psychophysiologic measures. © 1997 Elsevier Science B.V. Skin conductance (SC) data are usually characterized by a sequence of overlapping phasic skin conductance responses (SCRs) overlying a tonic component. The variability of SCR shapes hereby complicates the proper decomposition of SC data. A method is proposed for full decomposition of SC data into tonic and phasic components. A two-compartment diffusion model was found to adequately describe a standard SCR shape based on the process of sweat diffusion. Nonnegative deconvolution is used to decompose SC data into discrete compact responses and at the same time assess deviations from the standard SCR shape, which could be ascribed to the additional process of pore opening. Based on the result of single non-overlapped SCRs, response parameters can be estimated precisely as shown in a paradigm with varying inter-stimulus intervals. We describe a new method for measuring skin conductance responses, designed to overcome the problem of overlapping skin conductance responses. The method relies on the assumptions that the underlying sudomotor nerve signal has a shorter time-constant than the skin conductance signal itself, and that the sudomotor bursts arrive as discrete, separated events. By converting the skin conductance signal into a time-series with a shorter time-constant, we are able to extract the separated peaks in the estimated underlying driver signal. The separated driver peaks are then used to re-estimate each individual skin conductance response. The method is automated and applied to a normative database of 735 subjects, for which skin conductance was measured during an auditory oddball paradigm. Goal: This paper reports on a novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization. EDA can be considered as 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.