5 May 2017 Artifact detection in electrodermal activity using sparse recovery
Author Affiliations +
Abstract
Electrodermal Activity (EDA) – a peripheral index of sympathetic nervous system activity - is a primary measure used in psychophysiology. EDA is widely accepted as an indicator of physiological arousal, and it has been shown to reveal when psychologically novel events occur. Traditionally, EDA data is collected in controlled laboratory experiments. However, recent developments in wireless biosensing have led to an increase in out-of-lab studies. This transition to ambulatory data collection has introduced challenges. In particular, artifacts such as wearer motion, changes in temperature, and electrical interference can be misidentified as true EDA responses. The inability to distinguish artifact from signal hinders analyses of ambulatory EDA data. Though manual procedures for identifying and removing EDA artifacts exist, they are time consuming – which is problematic for the types of longitudinal data sets represented in modern ambulatory studies. This manuscript presents a novel technique to automatically identify and remove artifacts in EDA data using curve fitting and sparse recovery methods. Our method was evaluated using labeled data to determine the accuracy of artifact identification. Procedures, results, conclusions, and future directions are presented.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Malia Kelsey, Malia Kelsey, Richard Vincent Palumbo, Richard Vincent Palumbo, Alberto Urbaneja, Alberto Urbaneja, Murat Akcakaya, Murat Akcakaya, Jeannie Huang, Jeannie Huang, Ian R. Kleckner, Ian R. Kleckner, Lisa Feldman Barrett, Lisa Feldman Barrett, Karen S. Quigley, Karen S. Quigley, Ervin Sejdic, Ervin Sejdic, Matthew S. Goodwin, Matthew S. Goodwin, } "Artifact detection in electrodermal activity using sparse recovery", Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 102110D (5 May 2017); doi: 10.1117/12.2264027; https://doi.org/10.1117/12.2264027
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