Heart Rate Variability Analysis: How Much Artifact Can We Remove?
| Autor: | Michael Sabbaj, Steven D. Baker, Ryan Dehart, David C. Sheridan, Amber Lin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Artifact (error) 020205 medical informatics business.industry 02 engineering and technology 030204 cardiovascular system & hematology Audiology Suicidality Standard deviation 03 medical and health sciences Psychiatry and Mental health 0302 clinical medicine Cohort Artifact 0202 electrical engineering electronic engineering information engineering Medicine Heart rate variability Observational study Original Article Mental health business Biological Psychiatry Wearable technology circulatory and respiratory physiology |
| Zdroj: | Psychiatry Investigation |
| ISSN: | 1976-3026 1738-3684 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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