Abstract
The importance of medical wearable sensors is increasing in aiding both diagnostic and therapeutic protocols, in a wide area of health applications. Among them, the acquisition and analysis of electrodermal activity (EDA) may help in detecting seizures and different human emotional states. Nonnegative deconvolution represents an important step needed for decomposing the measured galvanic skin response (GSR) in its tonic and phasic components. In particular, the phasic component, also known as skin conductance response (SCR), is related to the sympathetic nervous system (SNS) activity, since it can be modeled as the linear convolution between the SCR driver events, modeled by sparse impulse signals, with an impulse response representing the sudomotor SNS innervation. In this paper, we propose a novel method for implementing this deconvolution by an adaptive filter, determined by solving a linear prediction problem, which results independent on the impulse response parameters, usually represented by sampling the biexponential Bateman function. The performance of the proposed approach is evaluated by using both synthetic and experimental data.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Savazzi, P., Vasile, F., Brondino, N., Vercesi, M., Politi, P. (2019). Estimation of Skin Conductance Response Through Adaptive Filtering. In: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_17
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DOI: https://doi.org/10.1007/978-3-030-34833-5_17
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