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Introduction: An analysis of Heart Rate Variability (HRV) is widely used in clinical and research. To properly calculate HRV parameters, the RR intervals series must be properly preprocessed. There are already automated tools for correcting these artifacts; however, they are not fully transparent and fully customizable. To address these limitations, we introduce CoRRection, a semi-automatic tool for RR interval correction, which integrates both automatic and manual approaches providing greater control over which intervals are corrected. Material and Methods: The application offers detection and correction methods. Additionally, it allows to manually remove an artifact before applying a correction method. It also allows to clean artifacts in shorter segments which enables applying different detection methods in one example, e.g., gathered during intense exercise. The application provides a test to assess signal stationarity (Augmented Dickey-Fuller test). After signal processing the report is created, containing the number of probes deleted and modified. Results: The proposed CoRRection application allows both technical and non-technical users to preprocess RR signals with a better control over the process by enabling the use of semi-automatic approach. A few examples of studies which required Correction’s unique approach of semi-automatic correction were presented. The need for different methods and not only considering different examinations, but also different segments within one recording was emphasized. Conclusions: We have presented a new application designed to preprocess RR intervals signal with a few popular detection and correction methods. This approach enables a good compromise between a precise manual approach and a less time-consuming automatic approach. The semi-automatic method of artifact correction empowers users to explore multiple identification and correction methods (and to choose the one best fitted to the data or measurement conditions), and offers a better understanding of the examination preprocessing tools. This may also result in a better trust in the automatic approach among the users.
Słowa kluczowe
Rocznik
Tom
Strony
10--19
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
- Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland
autor
- Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, Warsaw, Poland
autor
- Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland
Bibliografia
- 1. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation. 1996;93(5):1043-1065. https://doi.org/10.1161/01.CIR.93.5.1043
- 2. Billman GE. Heart Rate Variability ? A Historical Perspective. Front Physiol. 2011;2. https://doi.org/10.3389/fphys.2011.00086
- 3. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-aho PO, Karjalainen PA. Kubios HRV – Heart rate variability analysis software. Comput Methods Programs Biomed. 2014;113(1):210-220. https://doi.org/10.1016/j.cmpb.2013.07.024
- 4. Rasmussen JH, Rosenberger K, Langbein J. EasieRR: An open-source software for noninvasive heart rate variability assessment. Codling E, ed. Methods Ecol Evol. 2020;11(6):773-782. https://doi.org/10.1111/2041-210X.13393
- 5. Kaufmann T, Sütterlin S, Schulz SM, Vögele C. ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis. Behav Res Methods. 2011;43(4):1161-1170. https://doi.org/10.3758/s13428-011-0107-7
- 6. Klein J. Improving the reproducibility of findings by updating research methodology. Qual Quant. 2022;56(3):1597-1609. https://doi.org/10.1007/s11135-021-01196-6
- 7. Giles DA, Draper N. Heart Rate Variability During Exercise: A Comparison of Artefact Correction Methods. J Strength Cond Res. 2018;32(3):726-735. https://doi.org/10.1519/JSC.0000000000001800
- 8. Martinez P, Grinand M, Cheggour S, Taieb J, Gourjon G. How to properly evaluate cardiac vagal tone in oncology studies: a state-of-the-art review. J Natl Cancer Cent. 2024;4(1):36-46. https://doi.org/10.1016/j.jncc.2024.02.002
- 9. Lipponen JA, Tarvainen MP. A robust algorithm for heart rate variability time series artefact correction using novel beat classification. J Med Eng Technol. 2019;43(3):173-181. https://doi.org/10.1080/03091902.2019.1640306
- 10. Jarosław Piskorski, Przemysław Guzik. Filtering Poincaré plots. https://doi.org/10.12921/CMST.2005.11.01.39-48
- 11. Rincon Soler AI, Silva LEV, Fazan R, Murta LO. The impact of artifact correction methods of RR series on heart rate variability parameters. J Appl Physiol. 2018;124(3):646-652. https://doi.org/10.1152/japplphysiol.00927.2016
- 12. Silva LEV, Fazan R, Marin-Neto JA. PyBioS: A freeware computer software for analysis of cardiovascular signals. Comput Methods Programs Biomed. 2020;197:105718. https://doi.org/10.1016/j.cmpb.2020.105718
- 13. Peltola MA. Role of editing of R–R intervals in the analysis of heart rate variability. Front Physiol. 2012;3. https://doi.org/10.3389/fphys.2012.00148
- 14. Magagnin V, Bassani T, Bari V, et al. Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices. Physiol Meas. 2011;32(11):1775-1786. https://doi.org/10.1088/0967-3334/32/11/S05
- 15. Li K, Rüdiger H, Ziemssen T. Spectral Analysis of Heart Rate Variability: Time Window Matters. Front Neurol. 2019;10:545. https://doi.org/10.3389/fneur.2019.00545
- 16. Josef Perktold, Skipper Seabold, Kevin Sheppard, et al. statsmodels/statsmodels: Release 0.14.2. Published online April 17, 2024. https://doi.org/10.5281/ZENODO.593847
- 17. Prabhakaran S. Augmented Dickey-Fuller (ADF) Test - Must Read Guide - ML+. Machine Learning Plus. November 2, 2019. Accessed September 15, 2024. https://www.machinelearningplus.com/time-series/augmented-dickey-fuller-test/
- 18. Berntson GG, Quigley KS, Jang JF, Boysen ST. An Approach to Artifact Identification: Application to Heart Period Data. Psychophysiology. 1990;27(5):586-598. https://doi.org/10.1111/j.1469-8986.1990.tb01982.x
- 19. Christie IC, Gianaros PJ. PhysioScripts: An extensible, open source platform for the processing of physiological data. Behav Res Methods. 2013;45(1):125-131. https://doi.org/10.3758/s13428-012-0233-x
- 20. Jovic A, Bogunovic N. HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm. In: Peleg M, Lavrač N, Combi C, eds. Artificial Intelligence in Medicine. Vol 6747. Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2011:96-100. https://doi.org/10.1007/978-3-642-22218-4_13
- 21. Rodríguez-Liñares L, Lado MJ, Vila XA, Méndez AJ, Cuesta P. gHRV: Heart rate variability analysis made easy. Comput Methods Programs Biomed. 2014;116(1):26-38. https://doi.org/10.1016/j.cmpb.2014.04.007
- 22. Pichot V, Roche F, Celle S, Barthélémy JC, Chouchou F. HRVanalysis: A Free Software for Analyzing Cardiac Autonomic Activity. Front Physiol. 2016;7. https://doi.org/10.3389/fphys.2016.00557
- 23. Heart Rate Variability Analysis with the HRV Toolkit. Accessed September 15, 2024. https://archive.physionet.org/tutorials/hrv-toolkit/
- 24. Vest AN, Da Poian G, Li Q, et al. An open source benchmarked toolbox for cardiovascular waveform and interval analysis. Physiol Meas. 2018;39(10):105004. https://doi.org/10.1088/1361-6579/aae021
- 25. Behar JA, Rosenberg AA, Weiser-Bitoun I, et al. PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data. Front Physiol. 2018;9:1390. https://doi.org/10.3389/fphys.2018.01390
- 26. Highlights — pyHRV - OpenSource Python Toolbox for Heart Rate Variability 0.4 documentation. Accessed September 15, 2024. https://pyhrv.readthedocs.io/en/latest/
- 27. Vollmer M. HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability. In: ; 2019. https://doi.org/10.22489/CinC.2019.032
- 28. McConnell M, Schwerin B, So S, Richards B. RR-APET - Heart rate variability analysis software. Comput Methods Programs Biomed. 2020;185:105127. https://doi.org/10.1016/j.cmpb.2019.105127
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-f8fa50ae-7ac3-45f1-bf9e-588cdef00dfe
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