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ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis.
Open accessAbstract
The importance of appropriate handling of artifacts in interbeat interval (IBI) data must not be underestimated. Even a single artifact may cause unreliable heart rate variability (HRV) results. Thus, a robust artifact detection algorithm and the option for manual intervention by the researcher form key components for confident HRV analysis. Here, we present ARTiiFACT, a software tool for processing electrocardiogram and IBI data. Both automated and manual artifact detection and correction are available in a graphical user interface. In addition, ARTiiFACT includes time- and frequency-based HRV analyses and descriptive statistics, thus offering the basic tools for HRV analysis. Notably, all program steps can be executed separately and allow for data export, thus offering high flexibility and interoperability with a whole range of applications.
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“New softwares like gHRV (Rodriguez-Linares et al., 2014), a package for the R statistical environment, and ARTiiFACT (Kaufmann et al., 2011) are in development and offer other analysis tools such as different analysis options, visualize and export the HRV data, as well as the possibility to edit the source code for gHRV.”
From Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting (2017) · cites this paper as Kaufmann et al., 2011
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Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289
Calculation of signal detection theory measures
Stanislaw, H., & Todorov, N. (1999) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03207704 · Cited by 2,673
Thayer, J. F., & Lane, R. D. (2009) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2008.08.004 · Cited by 2,040
Heart rate variability as an index of regulated emotional responding
Appelhans, B. M., & Luecken, L. J. (2006) · Review of General Psychology · doi:10.1037/1089-2680.10.3.229 · Cited by 1,893
The Central Autonomic Network: Functional Organization, Dysfunction, and Perspective
BENARROCH, E. E. (1993) · Mayo Clinic Proceedings · doi:10.1016/s0025-6196(12)62272-1 · Cited by 1,534
Grossman, P., & Taylor, E. W. (2007) · Biological Psychology · doi:10.1016/j.biopsycho.2005.11.014 · Cited by 1,133
Malík, M. (1996) · Annals of Noninvasive Electrocardiology · doi:10.1111/j.1542-474x.1996.tb00275.x · Cited by 1,031
Software for advanced HRV analysis
Niskanen, J., Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2004) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2004.03.004 · Cited by 789
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Thayer, J. F., & Brosschot, J. F. (2005) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2005.04.014 · Cited by 616
The many metrics of cardiac chronotropy: A pragmatic primer and a brief comparison of metrics
Allen, J. J. B., Chambers, A. S., & Towers, D. N. (2007) · Biological Psychology · doi:10.1016/j.biopsycho.2006.08.005 · Cited by 491
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Viselli, L., Salfi, F., Naccarato, F., Arnone, B., Corigliano, D., Amicucci, G., Festucci, F., Colombo, C., Cellini, N., Tempesta, D., et al. (2025) · SLEEP · doi:10.1093/sleep/zsaf409 · Cited by 1
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Schneider, F., Martin, J., Skrzypczak, M., Hinzmann, D., Jordan, D., Wagner, K. J., & Schulz, C. M. (2018) · Journal of Psychophysiology · doi:10.1027/0269-8803/a000225 · Cited by 2
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Vargas, D. F. B., León-Ariza, H. H., Agudelo‐Otalora, L. M., Botero‐Rosas, D. A., & Moscoso-Barrera, W. D. (2022) · JMIR Biomedical Engineering · doi:10.2196/37453
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Rukh, L., Ahmad, Y., Zaman, U. K. U., Tiwana, M. I., & Naseem, A. (2024) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai62391.2024.10894663
Strouthos, A., Constantinou, E., Leonidou, C., Karekla, M., & Panayiotou, G. (2025) · Heart and Mind · doi:10.4103/hm.hm-d-25-00010
Silva, R., Costa, N., Sampaio, A., & Coutinho, J. F. P. (2025) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-025-09737-7 · Cited by 1
Toyama, T., Zhou, H. P., Sugimoto, S., Hayashi, K., Nagahara, M., Kuriyama, T., Mihara, G., Nakajima, K., & Ueta, T. (2026) · Eye · doi:10.1038/s41433-026-04245-2
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Lau, S. L. Y., Wong, A., Gu, Y.-F., & Hui, T. Y. (2026) · Limnology and Oceanography Methods · doi:10.1002/lom3.70055
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Dooms, Y., Qiu, L., Coppieters, I., Vergaelen, E., Claes, S. J., Dupont, P. J., Hehl, M., Cuypers, K., Engler, H., Dombrowski, K., et al. (2026) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2026.101299
Thomas, M. V., Schulz, A., Ryckeghem, D. M. L. V., Lutz, A., Lambracht, K., Adermann, J., & Vögele, C. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1813993
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Metadata
- Authors
- Kaufmann, T., Sütterlin, S., Schulz, S. M., & Vögele, C.
- Affiliations
- University of Würzburg (DE)
- University of Luxembourg (LU)
- Corresponding author
- Kaufmann, Tobias
- Venue
- Behavior Research Methods
- Published
- 2011-05-14
- Volume
- 43
- Issue
- 4
- Pages
- 1161-1170
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2073800110
- Cited by
- 284
- References
- 28
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 23 · 2023: 21 · 2022: 28 · 2021: 24 · 2020: 32 · 2019: 32 · 2018: 19 · 2017: 24 · 2016: 23 · 2015: 15 · 2014: 13 · 2013: 6 · 2012: 1
- FWCI
- 5.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- heart rate variability, artifact removal, graphical user interface software
- MeSH terms
- Algorithms, Heart Rate, Humans, Signal Processing, Computer-Assisted, Software, Electrocardiography, Ambulatory, Artifacts
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