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Cited by 121Emotion and Mood RecognitionHeart Rate Variability and Autonomic ControlMental Health Research Topics

Simple, Transparent, and Flexible Automated Quality Assessment Procedures for Ambulatory Electrodermal Activity Data

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Kleckner, Ian R., Jones, Rebecca M., Wilder-Smith, Oliver, Wormwood, Jolie B., Akcakaya, Murat, Quigley, Karen S., Lord, Catherine, Goodwin, Matthew S.·2018·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2017.2758643PMC PMC5880745
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Abstract

OBJECTIVE: Electrodermal activity (EDA) is a noninvasive measure of sympathetic activation often used to study emotions, decision making, and health. The use of "ambulatory" EDA in everyday life presents novel challenges-frequent artifacts and long recordings-with inconsistent methods available for efficiently and accurately assessing data quality. We developed and validated a simple, transparent, flexible, and automated quality assessment procedure for ambulatory EDA data. METHODS: A total of 20 individuals with autism (5 females, 5-13 years) provided a combined 181 h of EDA data in their home using the Affectiva Q Sensor across 8 weeks. Our procedure identified invalid data using four rules: First, EDA out of range; second, EDA changes too quickly; third, temperature suggests the sensor is not being worn; and fourth, transitional data surrounding segments identified as invalid via the preceding rules. We identified invalid portions of a pseudorandom subset of our data (32.8 h, 18%) using our automated procedure and independent visual inspection by five EDA experts. RESULTS: Our automated procedure identified 420 min (21%) of invalid data. The five experts agreed strongly with each other (agreement: 98%, Cohen's κ: 0.87) and, thus, were averaged into a "consensus" rating. Our procedure exhibited excellent agreement with the consensus rating (sensitivity: 91%, specificity: 99%, accuracy: 92%, κ: 0.739 [95% CI = 0.738, 0.740]). CONCLUSION: We developed a simple, transparent, flexible, and automated quality assessment procedure for ambulatory EDA data. SIGNIFICANCE: Our procedure can be used beyond this study to enhance efficiency, transparency, and reproducibility of EDA analyses, with free software available at http://www.cbslab.org/EDAQA.

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  • The quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project.

    “the quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project8–13”

    From Data quality evaluation in wearable monitoring (2022) · cites this paper as [12]

  • Artifacts in the EDA signal are often caused by connection loss of the electrodes to the skin due to body motion, and present as a sharp decrease or increase, or fall to a zero-line value altogether.

    “Artifacts in the EDA signal are thus often caused by connection loss of the electrodes to the skin due to, e.g., body motion, and present as a sharp decrease or increase, or fall to a zero-line value altogether12.”

    From Data quality evaluation in wearable monitoring (2022) · cites this paper as [12]

  • These kinds of artifacts can be detected by simple thresholding as well as analysis of the rate of amplitude change, similar to the artifacts seen in the temperature signal.

    “These kinds of artifacts can be detected by simple thresholding as well as analysis of the rate of amplitude change9,12, similar to the artifacts seen in the temperature signal.”

    From Data quality evaluation in wearable monitoring (2022) · cites this paper as [12]

  • Biosignal recordings may be of overall lesser quality in ultra-long-term monitoring.

    “Therefore, biosignal recordings may be of overall lesser quality12.”

    From Data quality evaluation in wearable monitoring (2022) · cites this paper as [12]

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Authors
Kleckner, I. R., Jones, R. M., Wilder-Smith, O., Wormwood, J. B., Akcakaya, M., Quigley, K. S., Lord, C., & Goodwin, M. S.
Affiliations
  • University of Rochester Medicine (US)
  • Weill Cornell Medicine (US)
  • Northeastern University (US)
  • University of Pittsburgh, USA
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018-07
Volume
65
Issue
7
Pages
1460-1467
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2017.2758643
PMC
PMC5880745
OpenAlex
W2764271683
Cited by
121
References
33
Citations by year
2026: 19 · 2025: 14 · 2024: 19 · 2023: 18 · 2022: 16 · 2021: 13 · 2020: 11 · 2019: 8 · 2018: 3
FWCI
7.78
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Simons Foundation (336363, 391635)
  • National Cancer Institute (R25 CA102618, UG1 CA189961)
  • National Institute of Mental Health (F32MH096533)
  • Army Research Institute for the Behavioral and Social Sciences (W911NF-16-1-0191)
  • National Institute of Nursing Research (NR013500)
  • National Institute on Deafness and Other Communication Disorders (P50 DC013027)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
electrodermal activity, data quality assessment, sensor temperature, autism spectrum disorder, sympathetic activation, reproducibility
MeSH terms
Data Accuracy, Adolescent, Algorithms, Autistic Disorder, Child, Female, Galvanic Skin Response, Humans, Quality Control, Signal Processing, Computer-Assisted, Reproducibility of Results, Monitoring, Ambulatory

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