Nicolas J C Stapelberg1,2, David L Neumann3, David H K Shum3, Harry McConnell4, Ian Hamilton-Craig5. 1. Bond University Faculty of Health Sciences and Medicine, Gold Coast, QLD, Australia. 2. Gold Coast Hospital and Health Service, Gold Coast, QLD, Australia. 3. School of Applied Psychology and Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia. 4. School of Medicine, Griffith University, Gold Coast, QLD, Australia. 5. School of Medicine Flinders University, Adelaide, SA, Australia.
Abstract
BACKGROUND: Artifact is common in cardiac RR interval data derived from 24-hr recordings and has a significant impact on heart rate variability (HRV) measures. However, the relative impact of progressively added artifact on a large group of commonly used HRV measures has not been assessed. This study compared the relative sensitivity of 38 commonly used HRV measures to artifact to determine which measures show the most change with increasing increments of artifact. A secondary aim was to ascertain whether short-term and long-term HRV measures, as groups, share similarities in their sensitivity to artifact. METHODS: Up to 10% of artifact was added to 20 artificial RR (ARR) files and 20 human cardiac recordings, which had been assessed for artifact by a cardiac technician. The added artifact simulated deletion of RR intervals and insertion of individual short RR intervals. Thirty-eight HRV measures were calculated for each file. Regression analysis was used to rank the HRV measures according to their sensitivity to artifact as determined by the magnitude of slope. RESULTS: RMSSD, SDANN, SDNN, RR triangular index and TINN, normalized power and relative power linear measures, and most nonlinear methods examined are most robust to artifact. CONCLUSION: Short-term time domain HRV measures are more sensitive to added artifact than long-term measures. Absolute power frequency domain measures across all frequency bands are more sensitive than normalized and relative frequency domain measures. Most nonlinear HRV measures assessed were relatively robust to added artifact, with Poincare plot SD1 being most sensitive.
BACKGROUND: Artifact is common in cardiac RR interval data derived from 24-hr recordings and has a significant impact on heart rate variability (HRV) measures. However, the relative impact of progressively added artifact on a large group of commonly used HRV measures has not been assessed. This study compared the relative sensitivity of 38 commonly used HRV measures to artifact to determine which measures show the most change with increasing increments of artifact. A secondary aim was to ascertain whether short-term and long-term HRV measures, as groups, share similarities in their sensitivity to artifact. METHODS: Up to 10% of artifact was added to 20 artificial RR (ARR) files and 20 human cardiac recordings, which had been assessed for artifact by a cardiac technician. The added artifact simulated deletion of RR intervals and insertion of individual short RR intervals. Thirty-eight HRV measures were calculated for each file. Regression analysis was used to rank the HRV measures according to their sensitivity to artifact as determined by the magnitude of slope. RESULTS: RMSSD, SDANN, SDNN, RR triangular index and TINN, normalized power and relative power linear measures, and most nonlinear methods examined are most robust to artifact. CONCLUSION: Short-term time domain HRV measures are more sensitive to added artifact than long-term measures. Absolute power frequency domain measures across all frequency bands are more sensitive than normalized and relative frequency domain measures. Most nonlinear HRV measures assessed were relatively robust to added artifact, with Poincare plot SD1 being most sensitive.
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