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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Banou, S., et al.: Beamforming galvanic coupling signals for IOMT implant-to-relay communication. IEEE Sen. J. 1 (2019). https://doi.org/10.1109/JSEN.2018.2886561
Benedek, M., Kaernbach, C.: A continuous measure of phasic electrodermal activity. J. Neurosci. Methods 190(1), 80–91 (2010)
Benedek, M., Kaernbach, C.: Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology 47(4), 647–658 (2010)
Boucsein, W.: Electrodermal Activity. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-1126-0
Haykin, S.: Adaptive Filter Theory, 4th edn. Prentice Hall, Upper Saddle River (2002)
Hernando-Gallego, F., Luengo, D., Arts-Rodrguez, A.: Feature extraction of galvanic skin responses by nonnegative sparse deconvolution. IEEE J. Biomed. Health Inform. 22(5), 1385–1394 (2018). https://doi.org/10.1109/JBHI.2017.2780252
Jain, S., Oswal, U., Xu, K.S., Eriksson, B., Haupt, J.: A compressed sensing based decomposition of electrodermal activity signals. IEEE Trans. Biomed. Eng. 64(9), 2142–2151 (2017). https://doi.org/10.1109/TBME.2016.2632523
Kappas, A., Kster, D., Basedow, C., Dente, P.: A validation study of the affective q-sensor in different social laboratory situations (2013)
McCarthy, C., Pradhan, N., Redpath, C., Adler, A.: Validation of the empatica E4 wristband. In: 2016 IEEE EMBS International Student Conference (ISC), pp. 1–4, May 2016. https://doi.org/10.1109/EMBSISC.2016.7508621
Nishiyama, T., Sugenoya, J., Matsumoto, T., Iwase, S., Mano, T.: Irregular activation of individual sweat glands in human sole observed by a videomicroscopy. Auton. Neurosci. 88(1–2), 117–126 (2001)
Park, I.M., Seth, S., Paiva, A.R.C., Li, L., Principe, J.C.: Kernel methods on spike train space for neuroscience: a tutorial. IEEE Signal Process. Mag. 30(4), 149–160 (2013). https://doi.org/10.1109/MSP.2013.2251072
Sano, A., et al.: Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. J. Med. Internet Res. 20(6), e210 (2018)
Sidis, B.: The nature and cause of the galvanic phenomenon. J. Abnorm. Psychol. 5(2), 6974 (1910). https://doi.org/10.1037/h0075352
Swaminathan, M., Vizziello, A., Duong, D., Savazzi, P., Chowdhury, K.R.: Beamforming in the body: energy-efficient and collision-free communication for implants. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp. 1–9, May 2017. https://doi.org/10.1109/INFOCOM.2017.8056989
Wright, J.J., et al.: Toward an integrated continuum model of cerebral dynamics: the cerebral rhythms, synchronous oscillation and cortical stability. BioSystems 63(1–3), 71–88 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-34833-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34832-8
Online ISBN: 978-3-030-34833-5
eBook Packages: Computer ScienceComputer Science (R0)