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Model- based filtering for artifact and noise suppression with state estimation for electrodermal activity measurements in real time | IEEE Conference Publication | IEEE Xplore

Model- based filtering for artifact and noise suppression with state estimation for electrodermal activity measurements in real time


Abstract:

Measurement of electrodermal activity (EDA) has recently made a transition from the laboratory into daily life with the emergence of wearable devices. Movement and nongel...Show More

Abstract:

Measurement of electrodermal activity (EDA) has recently made a transition from the laboratory into daily life with the emergence of wearable devices. Movement and nongelled electrodes make these devices more susceptible to noise and artifacts. In addition, real-time interpretation of the measurement is needed for user feedback. The Kalman filter approach may conveniently deal with both these issues. This paper presents a biophysical model for EDA implemented in an extended Kalman filter. Employing the filter on data from Physionet along with simulated noise and artifacts demonstrates noise and artifact suppression while implicitly providing estimates of model states and parameters such as the sudomotor nerve activation.
Date of Conference: 25-29 August 2015
Date Added to IEEE Xplore: 05 November 2015
ISBN Information:

ISSN Information:

PubMed ID: 26736861
Conference Location: Milan, Italy

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