Effect of Motion Artifact on Variation in Heart Rate Variability Parameters
Jae Mok Ahn1, Jeom Keun Kim2
1Jse Mok Ahn*, School of Software, Hallym University, Chuncheon-si, Gangwon-do, South Korea.
2Jeom Keun Kim, School of Software, Hallym University, Chuncheon-si, Gangwon-do, South Korea.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4611-4616 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9840038620/2020©BEIESP | DOI: 10.35940/ijrte.F9840.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The heart rate variability (HRV) is a noninvasive way properly for investigating the activity of the autonomic nervous system (ANS) as well as to predict cardiovascular diseases. To guarantee an accurate HRV analysis, a motion artifact-free HRV recording must be obtained. However, complete removal of a motion artifact is impossible when measuring heartbeats for 5 min, and the motion artifact due to sudden ANS activity must be taken into consideration for the HRV parameters. And, the ANS balance has thus far been evaluated by each individual HRV parameter calculated for a single 5 min HRV segment, leading to the dynamic activity of the ANS within the same period being ignored. Therefore, to resolve this problem, HRV parameters for ultra-short-term segments that are short enough to reflect a sudden motion artifact must be analyzed. The aim of the present study was to evaluate the effects of a motion artifact on the variation in HRV parameters to provide detailed information on ANS activity. The 121 ultra-short-term HRV segments were created by moving a 1-min window forward by a time shift interval of 2 s for the entire 5 min HRV segment. The ratios of Ln LF to Ln HF in these ultra-short-term segments and a single 5 min segment with a motion artifact were 0.89 and 1.06, respectively, while those in a motion artifact-free HRV segment were 0.75 and 0.93, respectively. This variation test for a short-term motion artifact and motion artifact-free HRV dataset was found to affect the SDNN (7.73 and 2.68), SD2 (11.44 and 4.42), TINN (40.33 and 9.92), and Ln HF (0.37 and 0.13) the most in terms of the standard deviation, respectively. Taken together, the mean HRV parameters of many ultra-short-term segments might play an important role in evaluating dynamic ANS activities within a short-term segment, avoiding the false conclusions made by the traditional HRV analysis.
Keywords: Autonomic Nervous System, Heart Rate Variability, Parasympathetic Nervous Branch, Sympathetic Nervous Branch.
Scope of the Article: Autonomic Computing.