Database Open Access

Electrocardiogram, skin conductance and respiration from spider-fearful individuals watching spider video clips

Frank R Ihmig Antonio Gogeascoechea Sarah Schäfer Johanna Lass-Hennemann Tanja Michael

Published: June 5, 2020. Version: 1.0.0


When using this resource, please cite: (show more options)
Ihmig, F. R., Gogeascoechea, A., Schäfer, S., Lass-Hennemann, J., & Michael, T. (2020). Electrocardiogram, skin conductance and respiration from spider-fearful individuals watching spider video clips (version 1.0.0). PhysioNet. https://doi.org/10.13026/sq6q-zg04.

Additionally, please cite the original publication:

Ihmig, F. R., Gogeascoechea H., A., Neurohr-Parakenings, F., Schäfer, S. K., Lass-Hennemann, J., & Michael, T. (2020). On-line anxiety level detection from biosignals: machine learning based on a randomized controlled trial with spider-fearful individuals. PLoS ONE, in press.

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Abstract

This project contains electrocardiogram, skin conductance and respiration as raw data (unfiltered, unprocessed) recorded from consented, spider-fearful individuals using the wearable BITalino biosignal measurement device (PLUX – Wireless Biosignals S.A., Lisbon, Portugal) with the sampling rate set to 100 Hz per channel having 10-bit resolution.

All subjects were introduced to the exposure procedure and rationale that is mainly based on the principles of the one-session exposure treatment. The main focus of this study was to investigate if the use of an HRV biofeedback intervention could be a promising therapeutic add-on to exposure therapy for specific phobias. Biosignals were collected at the Saarland University (Germany) from July 2017 to July 2018, and there are 57 subjects in total.

The dataset may support studies on mental health and psychophysiology related to other anxiety disorders, such as fear of flying, social phobia and posttraumatic stress disorder. 


Background

The aim of our project was to develop an algorithm for on-line anxiety level detection from biosignals recorded by low-cost commercial wearable sensors. Important features related to stress and anxiety can be derived from electrocardiogram (ECG), skin conductance, and respiration signals [1]. Heart rate (HR) and heart rate variability (HRV) can be extracted from ECG signals. Lower HR is associated with resting periods, whereas higher HR is related to emotional arousal [2]. Contrary to HR, HRV increases during resting periods and decreases during anxiety and stress periods. Similar to HR, the breathing rate is also well-known as an indicator of stress and anxiety [3]. Furthermore, skin conductance level and skin conductance response are widely used as an indicator of psychological stress and anxiety [4].

Based on our literature search, we found a lack of an appropriate dataset that is close to our envisioned application. There are datasets publicly available that are related to stress detection [5] but not explicitly to anxiety detection. Also, it is of advantage to have the same sensor hardware for dataset compilation as in the envisioned application. These were the reasons why we decided to conduct a randomized controlled trial with spider-fearful individuals to record and compile a dedicated dataset for our research and development purposes.


Methods

The Ethical Committee of the Faculty of Human Sciences of Saarland University granted the ethical approval for this study. All participants provided their written informed consent including their consent for publication.

All subjects watched a series of spider video clips. Each session started with a 1-minute demo clip followed by 16 1-minute spider video clips, all taken from TV documentaries showing detailed shots of spiders, and ended with a 5-minute resting phase. The sixteen clips with spiders were divided into two groups: clips 1-8 and clips 9-16. The order of the clips within each group was randomized.

Each type of data recorded from the wearable BITalino biosignal measurement device is saved in a separate file. We used the Bitalino (r)evolution BLE device (firmware version 5.1) including the related ECG, skin conductance and piezoelectric respiration sensors. Documentation is available on the manufacturers website [6]. 

Our dataset contains records from 57 subjects although 80 subjects aged between 18 and 40 years participated in the study. The subjects were divided into four groups of 20 subjects each. The records of group 1 were not considered because these subjects applied a trained breathing technique that affects HR and HRV characteristics. Another three records were disregarded due to technical problems during data acquisition. Each record is approximately 35 minutes long. For a full description of the methods, see [7]. For a detailed description of the study protocol, see [8].


Data Description

There is one folder for each subject. Each folder contains the following txt files that are provided as minimal anonymized dataset: 

  • BitalinoBR.txt: Respiration in % (column 1, indicates the deflection of the piezo sensor in the chest strap), value range from -50% to 50%; timestamp with format hhmmss.milliseconds (column 2, used for the mapping of the video clip time windows); label for RAW data (column 3, can be ignored).
  • BitalinoECG.txt: Electrocardiogram in mV (column 1), value range from -1.5 mV to 1.5 mV; timestamp with format hhmmss.milliseconds (column 2, used for the mapping of the video clip time windows); label for RAW data (column 3, can be ignored).
  • BitalinoGSR.txt: Skin conductance in uS (column 1), value range from -12.6 uS to 41 uS (negative values are sensor-related, values should be treated with offset correction); timestamp with format hhmmss.milliseconds (column 2, used for the mapping of the video clip time windows); label for RAW data (column 3, can be ignored).
  • Triggers.txt: Trigger-ID using CLIP-1 to CLIP-16 as anxiety stimulating video clips (column 1) and BIOFEEDBACK-REST for the resting phase; timestamp with format hhmmss for start (column 2) and end (column 3) of the video clip or resting phase.

Note: Although the sampling rate was set to 100 Hz, the timestamps in the txt files appear to be irregular. This is due to the generation of a Windows timestamp in our data acquisition software. We used the timestamps only for the assignment of the video clips and resting phase in combination with the Trigger-ID.

There is an additional txt file subject_groups.csv that shows the affiliation of the subjects to the groups.

Unfortunately, we are not able to provide the video clips because we do not have copyright to upload them. However, we provide a screenshot of one clip. Moreover, we can make the video clips available to other researchers for non-commercial use in research projects upon reasonable request.


Usage Notes

Recorded biosignals should be filtered according to state-of-the-art methods. Resulting data should be preprocessed according to the needs of the software framework used for machine learning. The paper describing this work including biosignal processing and feature extraction is available open-access [7]. The dataset may support studies on mental health and psychophysiology related to other anxiety disorders, such as fear of flying, social phobia and posttraumatic stress disorder.


Acknowledgements

This research was funded by the German Federal Ministry of Education and Research through an applied research grant (contract numbers 13GW0158B and 13GW0158C) within the program “Medical technology solutions for the digital healthcare”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 


Conflicts of Interest

The authors have no conflicts of interest to declare.


References

  1. Christy, T., & Kuncheva, L. I. (2014). Technological Advancements in Affective Gaming: A Historical Survey. GSTF Journal on Computing (JoC), 3(4), 29. https://doi.org/10.7603/s40601-013-0038-5
  2. Cacioppo, J. T., & McGuigan, F. J. (1980). Cognitive Psychophysiology: Principles of Covert Behavior. The American Journal of Psychology, 93(1), 173. https://doi.org/10.2307/1422117
  3. McDuff, D., Gontarek, S., & Picard, R. (2014). Remote measurement of cognitive stress via heart rate variability. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014, 2957–2960. https://doi.org/10.1109/EMBC.2014.6944243
  4. Liu, Y., & Du, S. (2018). Psychological stress level detection based on electrodermal activity. Behavioural Brain Research, 341, 50–53. https://doi.org/10.1016/j.bbr.2017.12.021
  5. Healey, J. A., & Picard, R. W. (2005). Detecting Stress During Real-World Driving Tasks Using Physiological Sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2), 156–166. https://doi.org/10.1109/TITS.2005.848368
  6. PLUX – Wireless Biosignals S.A., Lisbon, Portugal: https://bitalino.com/en/learn/documentation
  7. Ihmig, F. R., Gogeascoechea H., A., Neurohr-Parakenings, F., Schäfer, S. K., Lass-Hennemann, J., & Michael, T. (2020). On-line anxiety level detection from biosignals: machine learning based on a randomized controlled trial with spider-fearful individuals. PLoS ONE, in press.
  8. Schäfer, S. K., Ihmig, F. R., Lara H, K. A., Neurohr, F., Kiefer, S., Staginnus, M., Lass-Hennemann, J., & Michael, T. (2018). Effects of heart rate variability biofeedback during exposure to fear-provoking stimuli within spider-fearful individuals: Study protocol for a randomized controlled trial. Trials, 19(1), 184. https://doi.org/10.1186/s13063-018-2554-2

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Name Size Modified
Parent Directory
BitalinoBR.txt (download) 5.2 MB 2017-09-04
BitalinoECG.txt (download) 5.6 MB 2017-09-04
BitalinoGSR.txt (download) 6.4 MB 2017-09-04
Triggers.txt (download) 416 B 2017-09-04