Date: April 9 2020
Summary: A simple, transparent, flexible, and automated quality assessment procedure for ambulatory EDA data
Keywords: ##bibliography #eda #rochester #eda #rules #simple #transparent #autism #children #pediatrics #archive
I. R. Kleckner et al., "Simple, Transparent, and Flexible Automated Quality Assessment Procedures for Ambulatory Electrodermal Activity Data," IEEE Trans. Biomed. Eng., vol. 65, no. 7, pp. 1460–1467, Jul. 2018, doi: 10.1109/TBME.2017.2758643.
The authors also decided to create a very simple rule-based algorithm in an effort to make results easily reproducible and understandable to researchers of differing backgrounds. This algorithm for assessing data quality was as is follows:
Number | Rule | Rationale |
---|---|---|
1 | EDA is not within the range of | Prevent "floor" and "ceiling" artifacts |
2 | EDA fluctuates too fast | To prevent "jump" artifacts |
3 | Temperature not within 30-40 | Preserve best accuracy of data |
4 | EDA data within sec of invalid sections | Invalid sections based on rules |
If the data did not adhere to these rules, it was classified as artifact.
1. In Rule 1, 60 was chosen as an upper limit specifically because that was the upper limit of the Q Sensor.
2. Rule 2's rationale came from [1]–[3]
3. For Rule 3, their data recorded temperatures of 32 - 36 consistently for individuals
When collecting EDA data, the authors used solid conductive adhesive hydrogel Ag/AgCl electrodes (22 mm square; model A10040-5 from Vermed; Buffalo, NY).
Used 181 hours worth of data collected from children and adolescents with autism in an at-home environment.
Zelko, Jacob. Simple, Transparent, and Flexible Automated Quality Assessment Procedures for Ambulatory Electrodermal Activity. https://jacobzelko.com/04092020195141-transparent-eda-data. April 9 2020.
[1] W. Boucsein, Electrodermal Activity. Boston, MA: Springer US, 2012. doi: 10.1007/978-1-4614-1126-0.
[2] R. Kocielnik, N. Sidorova, F. M. Maggi, M. Ouwerkerk, and J. H. Westerink, “Smart technologies for long-term stress monitoring at work,” in Proceedings of the 26th IEEE international symposium on computer-based medical systems, 2013, pp. 53–58.
[3] F. H. Wilhelm and W. T. Roth, “Ambulatory assessment of clinical anxiety,” Ambul. Assess. Comput.-Assist. Psychol. Psychophysiological Methods Monit. Field Stud., pp. 317–345, 1996.