Pulse Amplitudes from electrodermal activity collected from healthy volunteer subjects at rest and under controlled sedation

Abstract

This dataset of electrodermal activity was collected from 11 healthy volunteer subjects who were awake and at rest in seated position and 11 different healthy volunteers who were under controlled propofol sedation. For the awake and at rest subjects, the activity was recorded from each subject's non-dominant hand for one hour at 256 Hz. For the controlled propofol sedation subjects, the activity was recorded from each subject's left hand for about 3-4 hours at 500 Hz. From the raw data, EDA pulses were extracted and the pulse times and amplitudes reported. Electrodermal activity measures changing electrical conductance of the skin as an indicator of sweat gland activity. Sweat glands are a primitive part of the fight-or-flight response. These data were collected as part of a larger study to understand and build computational models for autonomic nervous system activity (including electrodermal activity) with approval from the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (COUHES) and the Massachusetts General Hospital Human Research Committee.

Background

Electrodermal activity (EDA) is a direct read-out of body’s sympathetic nervous system measured as sweat-induced changes in the skin’s electrical conductance. It is controlled almost exclusively by the sympathetic branch of the autonomic nervous system as part of the "fight-or-flight" response [1]. The goal of our study was to understand the statistical properties of various physiological signals specifically with respect to autonomic nervous system control.

We seek to build computational models for these signals (including EDA) to accurately extract this autonomic information. To this end, we collected EDA data from 11 healthy volunteers while awake and seated at rest for one hour and 11 healthy volunteers undergoing controlled propofol sedation. We extracted pulse times and amplitudes from the data [2,3,4]. We would like to share this small database and code to allow other researchers to also explore and understand phasic EDA data and the rich information it contains. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states.

Methods

With the approval of the Massachusetts Institute of Technology (MIT) Institutional Review Board, we collected EDA data from 12 healthy volunteers between the ages of 22 and 34 while awake and at rest. We used the FDA-approved Neurofeedback System developed by Thought Technology Ltd. Electrodes were connected to the second most distal phalange of the second and fourth digits of each subject’s non-dominant hand.

Approximately one hour of EDA data was collected at 256 Hz. Subjects were seated upright and instructed to remain awake. They were allowed to read, meditate, or watch something on a laptop or tablet, but not to write with the instrumented hand. We assumed skin and ambient temperature were constant for the duration of the experiment.

One subject's data were not included in the database because they informed us after the data collection that they occasionally experience a Raynaud's type phenomenon. This would affect the quality of their EDA data. Data from the remaining 11 subjects are included in this database as extracted pulse times and amplitudes.

With the approval of the Massachusetts General Hospital (MGH) Institutional Review Board (IRB), we collected EDA data from 11 healthy volunteers under controlled propofol sedation in stages [5]. We used the FDA-approved BedMaster system. Electrodes were connected to the palm of each subject's left hand.

Approximately 3-4 hours of EDA data were collected at 500 Hz. We assumed ambient temperature was constant for the duration of the experiment. Data from the all 11 subjects are included in this database as extracted pulse times and amplitudes.

Data Description

The data were collected with approval from the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (COUHES) and the Massachusetts General Hospital Human Research Committee. All subjects provided written informed consent. The dataset comprises:

• 22 distinct subjects
• For the at rest dataset, per subject:
  • Extracted EDA pulse times
  • Extracted EDA pulse amplitudes
• For the propofol sedation dataset, per subject:
  • Extracted EDA pulse times
  • Extracted EDA pulse amplitudes

Part of these data overlaps with [6]; however, these data contain the extracted pulse times and amplitudes, whiles [6] additionally contains the raw unprocessed data and separated tonic and phasic components prior to extraction of pulse times and amplitudes.

For the at rest dataset, the data are purely observational; no specific stimuli were applied. We did not control what the subjects were reading or watching during the data collection. For the propofol sedation dataset, the propofol concentration was changed over time and is described in [5].

Format:

• The datasets are separated by folder, 'At-Rest' or 'Propofol-Sedation'.
• The pulse times and pulse amplitude files are named pulse_times_##.csvand pulse_amps_##.csv respectively. The units for pulse times are in seconds.

Usage Notes

These data have been used in a number of published works to build a new physiology-driven framework for EDA analysis, specifically [2], [3], [4], [7]. In these studies, a new point process framework rooted in sweat gland physiology was developed and validated, including a goodness-of-fit analysis. These data can be used by others in the community to further build upon these point process methods with the extracted pulse times and amplitudes. One known limitation of these data is that these are all observational datasets, meaning that there are no clearly annotated controlled stimuli. Another dataset of interest is [6], which contains raw EDA data and separated tonic and phasic components.

Release Notes

This is the first release of this dataset.

Acknowledgements

We would like to thank the Massachusetts Institute of Technology Clinical Research Center Staff. This work was partially funded by funds from the Picower Institute for Learning and Memory, the National Science Foundation Graduate Research Fellowship Program, and National Institutes of Health Award P01-GM118629.

Conflicts of Interest

Patent filed 07-15-2020 (PCT/US2020/042031).

References

1. W. Boucsein, Electrodermal Activity. (Springer, 2012).
2. S. Subramanian, R. Barbieri, E. N. Brown, Point process temporal structure characterizes electrodermal activity. Proceedings of the National Academy of Science. (2020).
3. S Subramanian, PL Purdon, R Barbieri, EN Brown. A Model-Based Approach for Pulse Selection from Electrodermal Activity. IEEE Trans Biomed Eng. 2021 Apr 6. doi: 10.1109/TBME.2021.3071366.
4. S Subramanian, PL Purdon, R Barbieri, EN Brown. Elementary Integrate-and-Fire Process Underlies Pulse Amplitudes in Electrodermal Activity. Biorxiv, 2020. doi: https://doi.org/10.1101/2021.01.06.425499
5. PL Purdon et al., Electroencephalogram Signatures of Loss and Recovery of Consciousness from Propofol. PNAS. 110, E1142-1151 (2013).
6. Subramanian, S., Barbieri, R., & Brown, E. (2020). Electrodermal Activity of Healthy Volunteers while Awake and at Rest (version 1.0). PhysioNet. https://doi.org/10.13026/arty-2540.
7. S Subramanian, PL Purdon, R Barbieri, EN Brown. Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness. Biorxiv, 2020. doi: https://doi.org/10.1101/2020.11.03.367607