The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Psychosomatics and psychopathology: looking up and down from the brain
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
Cited by
Sorted by citations
Laborde, S., Mosley, E., & Thayer, J. F. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00213 · Cited by 2,336
“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 gH…”
MethodA comprehensive survey of wearable and wireless ECG monitoring systems for older adults
Baig, M. M., Gholamhosseini, H., & Connolly, M. J. (2013) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-1021-6 · Cited by 284
Heart Rate Variability as Indicator of Clinical State in Depression
Hartmann, R., Schmidt, F. M., Sander, C., & Hegerl, U. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00735 · Cited by 262
Quigley, K. S., Gianaros, P. J., Norman, G. J., Jennings, J. R., Berntson, G. G., & de Geus, E. J. C. (2024) · Psychophysiology · doi:10.1111/psyp.14604 · Cited by 281
Bornemann, B., & Singer, T. (2017) · Psychophysiology · doi:10.1111/psyp.12790 · Cited by 204
Milstein, N., & Gordon, I. (2020) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2020.00148 · Cited by 180
HRVanalysis: A Free Software for Analyzing Cardiac Autonomic Activity
Pichot, V., Roche, F., Celle, S., Barthélemy, J., & Chouchou, F. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00557 · Cited by 187
Stressing the accuracy: Wrist‐worn wearable sensor validation over different conditions
Menghini, L., Gianfranchi, E., Cellini, N., Patron, E., Tagliabue, M., & Sarlo, M. (2019) · Psychophysiology · doi:10.1111/psyp.13441 · Cited by 157
Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience
Massaro, S., & Pecchia, L. (2016) · Organizational Research Methods · doi:10.1177/1094428116681072 · Cited by 178
Engert, V., Kok, B. E., Papassotiriou, I., Chrousos, G. P., & Singer, T. (2017) · Science Advances · doi:10.1126/sciadv.1700495 · Cited by 151
Müller, L. E., Schulz, A., Andermann, M., Gäbel, A., Gescher, D. M., Spohn, A., Herpertz, S. C., & Bertsch, K. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.1252 · Cited by 141
Affective Instability in Daily Life Is Predicted by Resting Heart Rate Variability
Koval, P., Ogrinz, B., Kuppens, P., den Bergh, O. V., Tuerlinckx, F., & Sütterlin, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081536 · Cited by 157
Self-citationLumma, A., Kok, B. E., & Singer, T. (2015) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2015.04.017 · Cited by 149
Heart rate variability: Measurement and emerging use in critical care medicine
Johnston, B. W., Barrett‐Jolley, R., Krige, A., & Welters, I. D. (2019) · Journal of the Intensive Care Society · doi:10.1177/1751143719853744 · Cited by 147
Zimmer, P., Buttlar, B., Halbeisen, G., Walther, E., & Domes, G. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2018.11.010 · Cited by 119
Nguyen, T., Abney, D. H., Salamander, D., Bertenthal, B. I., & Hoehl, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118599 · Cited by 104
Engert, V., Koester, A. M., Riepenhausen, A., & Singer, T. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2016.08.029 · Cited by 103
Wojniusz, S., Callens, N., Sütterlin, S., Andersson, S., Schepper, J. D., Gies, I., Vanbesien, J., Waele, K. D., Aken, S. V., Craen, M., et al. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01053 · Cited by 98
Self-citationPappens, M., Schroijen, M., Sütterlin, S., Smets, E., Van den Bergh, O., Thayer, J. F., & Van Diest, I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105054 · Cited by 91
Self-citationVahle‐Hinz, T., Bamberg, E., Dettmers, J., Friedrich, N., & Keller, M. (2014) · Journal of Occupational Health Psychology · doi:10.1037/a0036009 · Cited by 109
Ellis, R. J., Zhu, B., Koenig, J., Thayer, J. F., & Wang, Y. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/9/1827 · Cited by 85
HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability
Vollmer, M. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.032 · Cited by 85
Sensors Capabilities, Performance, and Use of Consumer Sleep Technology
de Zambotti, M., Cellini, N., Menghini, L., Sarlo, M., & Baker, F. C. (2020) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2019.11.003 · Cited by 95
Toohey, K. L., Pumpa, K. L., McKune, A. J., Cooke, J., Welvaert, M., Northey, J. M., Quinlan, C., & Semple, S. (2020) · BMC Cancer · doi:10.1186/s12885-020-07295-1 · Cited by 90
Reduced heart rate variability in schizophrenia and bipolar disorder compared to healthy controls
Quintana, D. S., Westlye, L. T., Kaufmann, T., Rustan, Ø. G., Brandt, C. L., Haatveit, B., Steen, N. E., & Andreassen, O. A. (2016) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12498 · Cited by 73
Self-citationEnhanced cortical processing of cardio-afferent signals in anorexia nervosa
Lutz, A., Schulz, A., Voderholzer, U., Koch, S., Dyck, Z. V., & Vögele, C. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.06.009 · Cited by 69
Self-citationSűdy, Á. R., Ella, K., Bódizs, R., & Káldi, K. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00950 · Cited by 65
Bornemann, B., Kok, B. E., Böckler, A., & Singer, T. (2016) · Biological Psychology · doi:10.1016/j.biopsycho.2016.07.004 · Cited by 62
Quintana, D. S., Elstad, M., Kaufmann, T., Brandt, C. L., Haatveit, B., Haram, M., Nerhus, M., Westlye, L. T., & Andreassen, O. A. (2016) · Scientific Reports · doi:10.1038/srep37212 · Cited by 61
Self-citationThe Association between Work-Related Rumination and Heart Rate Variability: A Field Study
Cropley, M., Plans, D., Morelli, D., Sütterlin, S., Inceoglu, I., Thomas, G., & Chu, C. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00027 · Cited by 60
Self-citationI'm too calm--let's take a risk! On the impact of state and trait arousal on risk taking.
Schmidt, B., Mussel, P., & Hewig, J. (2013) · Psychophysiology · doi:10.1111/psyp.12032 · Cited by 64
Nasso, S., Vanderhasselt, M., Demeyer, I., & Raedt, R. D. (2018) · Emotion · doi:10.1037/emo0000448 · Cited by 66
Ectopic beats detection and correction methods: A review
Dib, N., & Reguig, F. B. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.01.008 · Cited by 59
Fusion of Scores in a Detection Context Based on Alpha Integration
Soriano, A., Vergara, L., Bouziane, A., & Salazar, A. (2015) · Neural Computation · doi:10.1162/neco_a_00766 · Cited by 58
Maier, S. U., & Hare, T. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2815-16.2016 · Cited by 59
Validation of Gazepoint low-cost eye-tracking and psychophysiology bundle
Cuve, H. C., Stojanov, J., Roberts-Gaal, X., Catmur, C., & Bird, G. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01654-x · Cited by 59
Duarte, J., & Pinto‐Gouveia, J. (2017) · Psychiatry Research · doi:10.1016/j.psychres.2017.07.077 · Cited by 66
PyBioS: A freeware computer software for analysis of cardiovascular signals
Silva, L. E. V., Fazan, R., & Marin‐Neto, J. A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105718 · Cited by 63
Effects of resting heart rate variability on performance in the P300 brain-computer interface
Kaufmann, T., Vögele, C., Sütterlin, S., Lukito, S., & Kübler, A. (2011) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2011.11.018 · Cited by 54
Self-citationCan words heal? Using affect labeling to reduce the effects of unpleasant cues on symptom reporting
Constantinou, E., Houte, M. V. D., Bogaerts, K., Diest, I. V., & den Bergh, O. V. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00807 · Cited by 54
Mussel, P., Ulrich, N., Allen, J. J. B., Osinsky, R., & Hewig, J. S. (2016) · Scientific Reports · doi:10.1038/srep29245 · Cited by 61
Tagliarolo, M., & McQuaid, C. D. (2015) · Marine Ecology Progress Series · doi:10.3354/meps11434 · Cited by 53
gHRV: heart rate variability analysis made easy.
Rodríguez-Liñares, L., Lado, M. J., Vila, X. A., Méndez, A. J., & Cuesta, P. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.04.007 · Cited by 52
De Witte, N. A. J., Sütterlin, S., Braet, C., & Mueller, S. C. (2016) · PLOS ONE · doi:10.1371/journal.pone.0164615 · Cited by 47
Self-citationPeak Detection and HRV Feature Evaluation on ECG and PPG Signals
Esgalhado, F., Batista, A. G., Vassilenko, V., Russo, S., & Ortigueira, M. D. (2022) · Symmetry · doi:10.3390/sym14061139 · Cited by 57
Michal, M., Koechel, A., Canterino, M., Adler, J., Reiner, I., Vossel, G., Beutel, M. E., & Gamer, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074331 · Cited by 53
Alterations in electrodermal activity and cardiac parasympathetic tone during hypnosis
Kekecs, Z., Szekely, A., & Varga, K. (2016) · Psychophysiology · doi:10.1111/psyp.12570 · Cited by 47
Houte, M. V. D., Bogaerts, K., Diest, I. V., Bie, J. D., Persoons, P., Oudenhove, L. V., & den Bergh, O. V. (2017) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000527 · Cited by 49
Rost, S., Ryckeghem, D. M. L. V., Schulz, A., Crombez, G., & Vögele, C. (2016) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2016.12.003 · Cited by 46
Self-citationGrassmann, M., Vlemincx, E., von Leupoldt, A., & Van den Bergh, O. (2017) · Applied Ergonomics · doi:10.1016/j.apergo.2016.09.006 · Cited by 45
Wölk, J., Sütterlin, S., Koch, S., Vögele, C., & Schulz, S. M. (2014) · Brain and Behavior · doi:10.1002/brb3.206 · Cited by 44
Self-citationVervoort, T., Trost, Z., Sütterlin, S., Caes, L., & Moors, A. (2014) · Pain · doi:10.1016/j.pain.2014.04.015 · Cited by 44
Self-citationTowards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification
Barua, S., Ahmed, M. U., & Begum, S. (2020) · Brain Sciences · doi:10.3390/brainsci10080526 · Cited by 49
Defensive functions provoke similar psychophysiological reactions in reaching and comfort spaces
Ruggiero, G., Rapuano, M., Cartaud, A., Coello, Y., & Iachini, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-83988-2 · Cited by 43
Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability*
Nagaraj, S. B., Biswal, S., Boyle, E. J., Zhou, D. W., McClain, L. M., Bajwa, E. K., Quraishi, S. A., Akeju, O., Barbieri, R., Purdon, P. L., et al. (2017) · Critical Care Medicine · doi:10.1097/ccm.0000000000002364 · Cited by 43
Altered autonomic arousal in psychosis: An analysis of vulnerability and specificity
Clamor, A., Hartmann, M. M., Köther, U., Otte, C., Moritz, S., & Lincoln, T. M. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.02.006 · Cited by 42
Nightmares do result in psychophysiological arousal: A multimeasure ambulatory assessment study
Paul, F., Alpers, G. W., Reinhard, I., & Schredl, M. (2019) · Psychophysiology · doi:10.1111/psyp.13366 · Cited by 46
Timmy’s in the well: Empathy and prosocial helping in dogs
Sanford, E. M., Burt, E. R., & Meyers-Manor, J. E. (2018) · Learning & Behavior · doi:10.3758/s13420-018-0332-3 · Cited by 41
Mayor, D. F., Panday, D., Kandel, H. K., Steffert, T., & Banks, D. (2021) · Entropy · doi:10.3390/e23030321 · Cited by 40
Resting vagal tone is negatively associated with suicide ideation
Forkmann, T., Meessen, J., Teismann, T., Sütterlin, S., Gauggel, S., & Mainz, V. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.01.032 · Cited by 40
Self-citationbiopeaks: a graphical user interface for feature extraction from heart- and breathing biosignals
Brammer, J. C. (2020) · The Journal of Open Source Software · doi:10.21105/joss.02621 · Cited by 39
Emotion Recognition in Human Computer Interaction Systems
Mikuckas, A., Mikuckienė, I., Venčkauskas, A., Kazanavičius, E., Lukas, R., & Plauska, I. (2014) · Elektronika ir Elektrotechnika · doi:10.5755/j01.eee.20.10.8878 · Cited by 36
Sense it and use it: interoceptive accuracy and sensibility in suicide ideators
Forkmann, T., Volz‐Sidiropoulou, E., Helbing, T., Drueke, B., Mainz, V., Rath, D., Gauggel, S., & Teismann, T. (2019) · BMC Psychiatry · doi:10.1186/s12888-019-2322-1 · Cited by 40
De Witte, N. A. J., Sütterlin, S., Braet, C., & Mueller, S. C. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.03.012 · Cited by 36
Self-citationBaig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F., & Lindén, M. (2017) · Health Informatics Journal · doi:10.1177/1460458217740722 · Cited by 33
Panayiotou, G., & Constantinou, E. (2017) · Psychophysiology · doi:10.1111/psyp.12887 · Cited by 35
Zimmer, P., Wu, C. C., & Domes, G. (2019) · Physiology & Behavior · doi:10.1016/j.physbeh.2019.112690 · Cited by 34
Scheveneels, S., Boddez, Y., Van Daele, T., & Hermans, D. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02849 · Cited by 33
Heart rate variability: a biomarker of dairy calf welfare
Clapp, J. B., Croarkin, S., Dolphin, C., & Lyons, S. K. (2014) · Animal Production Science · doi:10.1071/an14093 · Cited by 33
The relationship between heart rate variability and canine aggression
Craig, L., Meyers-Manor, J. E., Anders, K., Sütterlin, S., & Miller, H. C. (2017) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2016.12.015 · Cited by 35
Self-citationChiu, H. T., Ip, I. N., Ching, F. N. Y., Wong, B. P.-H., Lui, W.-H., Tse, C.-S., & Wong, S. W. H. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-022-05847-x · Cited by 32
Sætren, S. S., Sütterlin, S., Lugo, R. G., Prince‐Embury, S., & Makransky, G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00438 · Cited by 35
Self-citationBrain and Body Emotional Responses: Multimodal Approximation for Valence Classification
Sorinas, J., Ferrández, J. M., & Fernández, E. (2020) · Italian National Conference on Sensors · doi:10.3390/s20010313 · Cited by 27
Chelidoni, O., Plans, D., Ponzo, S., Morelli, D., & Cropley, M. (2020) · JMIR mhealth and uhealth · doi:10.2196/19412 · Cited by 37
Modeling the diving bradycardia: Toward an “oxygen-conserving breaking point”?
Costalat, G., Pichon, A., Joulia, F., & Lemaître, F. (2015) · European Journal of Applied Physiology · doi:10.1007/s00421-015-3129-5 · Cited by 32
Sahrmann, J. M., Niedbalski, A., Bradshaw, L., Johnson, R., & Deem, S. L. (2015) · Zoo Biology · doi:10.1002/zoo.21257 · Cited by 31
Bornemann, B., Kovacs, P., & Singer, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-44201-7 · Cited by 30
Autonomic dysfunction in ICU-acquired weakness: a prospective observational pilot study
Wieske, L., Yin, D. R. P. P. C. P., Verhamme, C., Schultz, M. J., van Schaik, I. N., & Horn, J. (2013) · Intensive Care Medicine · doi:10.1007/s00134-013-2991-z · Cited by 30
Overcoming Selfishness: Reciprocity, Inhibition, and Cardiac-Autonomic Control in the Ultimatum Game
Sütterlin, S., Herbert, C., Schmitt, M., Kübler, A., & Vögele, C. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00173 · Cited by 34
Self-citationSimulation Training in Neuroangiography—Validation and Effectiveness
Kreiser, K., Ströber, L., Gehling, K. G., Schneider, F., Kohlbecher, S., Schulz, C. M., Zimmer, C., & Kirschke, J. S. (2020) · Clinical Neuroradiology · doi:10.1007/s00062-020-00902-5 · Cited by 29
Szulczewski, M. T., & Rynkiewicz, A. (2018) · Psychophysiology · doi:10.1111/psyp.13221 · Cited by 53
Saleem, S., Khandoker, A. H., Alkhodari, M., Hadjileontiadis, L. J., & Jelinek, H. F. (2022) · Scientific Reports · doi:10.1038/s41598-022-21776-2 · Cited by 31
Endogenous Pain Modulation: Association with Resting Heart Rate Variability and Negative Affectivity
Houte, M. V. D., Oudenhove, L. V., Bogaerts, K., Diest, I. V., & den Bergh, O. V. (2017) · Pain Medicine · doi:10.1093/pm/pnx165 · Cited by 28
Wang, Y., Jiang, L., Ren, L., Pingao, H., Yu, M., Tian, L., Li, X., Xie, J., Fang, P., & Li, G. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3070541 · Cited by 27
Konstantinou, P., Trigeorgi, A., Georgiou, C., Gloster, A. T., Panayiotou, G., & Karekla, M. (2020) · Psychophysiology · doi:10.1111/psyp.13551 · Cited by 27
Escalona-Vargas, D., Bolin, E. H., Lowery, C. L., Siegel, E. R., & Eswaran, H. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abc353 · Cited by 26
Be still my heart: Cardiac regulation as a mode of uncertainty reduction
Corcoran, A. W., Macefield, V. G., & Hohwy, J. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-021-01888-y · Cited by 30
Prabakaran, A., & Rufus, E. (2021) · Sensor Review · doi:10.1108/sr-05-2021-0150 · Cited by 26
Putter, L. M. S. D., Vanderhasselt, M., Baeken, C., Raedt, R. D., & Koster, E. H. W. (2015) · Cognitive Therapy and Research · doi:10.1007/s10608-015-9710-8 · Cited by 27
Humeau‐Heurtier, A., Abraham, P., & Mahé, G. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2281620 · Cited by 25
Schneider, F., Martin, J., Hapfelmeier, A., Jordan, D., Schneider, G. H., & Schulz, C. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188635 · Cited by 28
Quintana, D. S., Elvsåshagen, T., Zak, N., Norbom, L. B., Pedersen, P. Ø., Quraishi, S. H., Bjørnerud, A., Malt, U. F., Groote, I. R., Kaufmann, T., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170921 · Cited by 27
Self-citationHRVTool – an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability
Vollmer, M. (2019) · doi:10.23919/cinc49843.2019.9005745 · Cited by 24
Altered regulation of negative affect in patients with fibromyalgia: A diary study
Rost, S., Crombez, G., Sütterlin, S., Vögele, C., Veirman, E., & Ryckeghem, D. M. L. V. (2020) · European Journal of Pain · doi:10.1002/ejp.1706 · Cited by 24
Self-citationVosseler, A., Zhao, D., Fritsche, L., Lehmann, R., Kantartzis, K., Small, D. M., Peter, A., Häring, H., Birkenfeld, A. L., Fritsche, A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-77430-2 · Cited by 24
Plans, D., Morelli, D., Sütterlin, S., Ollis, L., Derbyshire, G., & Cropley, M. (2018) · JMIR Formative Research · doi:10.2196/12227 · Cited by 25
Self-citationWarmer Environments Increase Implicit Mental Workload Even If Learning Efficiency Is Enhanced
Kimura, T., Takemura, N., Nakashima, Y., Kobori, H., Nagahara, H., Numao, M., & Shinohara, K. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00568 · Cited by 24
Tagliarolo, M., & McQuaid, C. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146341 · Cited by 23
Scoley, G., Gordon, A. W., & Morrison, S. J. (2019) · animal · doi:10.1017/s1751731119001071 · Cited by 23
Effects of glucose intake on stress reactivity in young, healthy men
von Dawans, B., Zimmer, P., & Domes, G. (2020) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2020.105062 · Cited by 23
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review