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Heart Rate Variability Analysis: How Much Artifact Can We Remove?

OBJECTIVE: Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objec...

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Autores principales: Sheridan, David C., Dehart, Ryan, Lin, Amber, Sabbaj, Michael, Baker, Steven D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Neuropsychiatric Association 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538246/
https://www.ncbi.nlm.nih.gov/pubmed/33017533
http://dx.doi.org/10.30773/pi.2020.0168
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author Sheridan, David C.
Dehart, Ryan
Lin, Amber
Sabbaj, Michael
Baker, Steven D.
author_facet Sheridan, David C.
Dehart, Ryan
Lin, Amber
Sabbaj, Michael
Baker, Steven D.
author_sort Sheridan, David C.
collection PubMed
description OBJECTIVE: Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology. METHODS: This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered. RESULTS: Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed. CONCLUSION: Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.
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spelling pubmed-75382462020-10-16 Heart Rate Variability Analysis: How Much Artifact Can We Remove? Sheridan, David C. Dehart, Ryan Lin, Amber Sabbaj, Michael Baker, Steven D. Psychiatry Investig Original Article OBJECTIVE: Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology. METHODS: This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered. RESULTS: Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed. CONCLUSION: Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality. Korean Neuropsychiatric Association 2020-09 2020-09-18 /pmc/articles/PMC7538246/ /pubmed/33017533 http://dx.doi.org/10.30773/pi.2020.0168 Text en Copyright © 2020 Korean Neuropsychiatric Association This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Sheridan, David C.
Dehart, Ryan
Lin, Amber
Sabbaj, Michael
Baker, Steven D.
Heart Rate Variability Analysis: How Much Artifact Can We Remove?
title Heart Rate Variability Analysis: How Much Artifact Can We Remove?
title_full Heart Rate Variability Analysis: How Much Artifact Can We Remove?
title_fullStr Heart Rate Variability Analysis: How Much Artifact Can We Remove?
title_full_unstemmed Heart Rate Variability Analysis: How Much Artifact Can We Remove?
title_short Heart Rate Variability Analysis: How Much Artifact Can We Remove?
title_sort heart rate variability analysis: how much artifact can we remove?
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538246/
https://www.ncbi.nlm.nih.gov/pubmed/33017533
http://dx.doi.org/10.30773/pi.2020.0168
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