Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects

Study Sample and Ethics Statement

The sample consisted of 71 university students, average age 20.38 years (SD = 2.96), 78.8% female. Subjects with previous cardio-vascular disorders and irregular ECG were excluded. The study has been approved by the Institutional Review Board of the Department of Psychology, University of Belgrade No. 2018-19. All participants signed Informed Consents in accordance with the Declaration of Helsinki.

In the course of visual examination, it was decided to discard ECG from one subject due to the presence of bigeminial arythmia, so further analysis was performed on 70 subjects instead of 71.

Measurement Setup

BIOPAC sensors (Biopac Systems Inc., Camino Goleta, CA, USA) were used for recording biosignals in another study (Bjegojević et al., 2020). Here, we used only ECG signals recorded in sitting relaxed position from standard bipolar Lead I using the BIOPAC MP150 unit with AcqKnowledge software and ECG 100C module with surface H135SG Ag/AgCl electrodes (Kendall/Covidien, Dublin, Ireland). In order to decrease skin-electrode impedance, the skin was cleaned with Nuprep gel (Weaver & Co., Aurora, USA) to reduce skin-electrode impedance. The sampling frequency was set at 2000 Hz and the gain was set to 1000.

ECG signals were recorded during relaxation in a sitting position and data were recorded during 2 min long intervals. More information is available in the article [1].

Dataset, Code, and Feature Extraction Instructions

1. analysisECG.R, function with analysis procedures written in R programming language
2. anec12919-sup-0001-supinfo.pdf, detailed ECG processing and feature extraction procedure (also available as supplementary material for article [1])
3. ecg_70.txt, .txt data file, text format
4. mainECG.R, a main program written in R programming language
5. R-studio-version-info.txt, the version of R Studio where the code was tested
6. R-version-info.txt , the version of R programming language where the code was tested

For ECG-based feature extraction, we used the following R packages:

1. signal - Signal Processing Functions (signal developers (2014). signal: Signal processing. http://r-forge.r-project.org/projects/signal/)
2. pracma - Practical Numerical Math Functions ( Borchers, H. W. (2019). Package ‘pracma’: Practical numerical math functions. R package version, 2(1). https://CRAN.R-project.org/package=pracma)

Please, note that the results of personality trait tests are not available in the current dataset. We are planning to open them in our future research. For more information and planned availability in open access, please, contact the corresponding author of [1] by e-mail (nadica.miljkovic@etf.bg.ac.rs).

Citing Instruction

If you find these signals and code useful for your own research or teaching class, please cite relevant dataset and supporting publications:

1. Boljanić, T., Miljković, N., Lazarević, L. B., Knežević, G., & Milašinović, G. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. Annals of Noninvasive Electrocardiology, 00, e12919. https://doi.org/10.1111/anec.12919

2. Bjegojević, B., Milosavljević, N., Dubljević, O., Purić, D., & Knežević, G. (2020). In pursuit of objectivity: Physiological measures as a means of emotion induction procedure validation. XXIVI Scientific Conference on Empirical Studies in Psychology, p. 17-19.

3. Boljanić, T., Miljković, N., Lazarević B. Lj., Knežević, G., & Milašinović, G. (2021). Surface electrocardiogram (ECG) dataset recorded during relaxation in 70 healthy subjects (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5599239