Relationships Between Autonomic Nervous System Indices Derived from ECG Signals

  • Chié KurosakaEmail author
  • Hiroyuki Kuraoka
  • Shimpei Yamada
  • Shinji Miyake
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 827)


We investigated the validity of heart rate variability (HRV) as an autonomic nervous system (ANS) assessment method and also investigated the correlations between all pairs of several indices obtained from ECG signals during a task performance. We recorded ECGs from 18 healthy participants during two 30-min mental tasks, i.e., a word processor typing task and a mental arithmetic task, and calculated five indices of HRV spectral analysis and five parameters from the Poincaré plot. In the results, significant correlations were found among the participants between LF and SD2 and between HF and SD1. There were no significant differences in the correlation coefficients between the two tasks, and no subjective scores correlated with physiological indices. The correlation coefficients between the sympathetic nervous system activity indices such as LF/HF ratio and CSI, however, were not very high, and these results were different from previous studies. Based on these results, we investigated the correlation coefficient time series in each participant and found that there were fluctuations in correlation coefficients between two indices which had been reported in other studies to indicate the same ANS activity. It was clarified that weak correlations among participants occurred by the fluctuations of correlation coefficients obtained in each participant.


Heart rate variability Poincaré plot Mental workload 



The data we analyzed was obtained in a collaborative study with Zheng Yi, DAIKIN Industries Ltd.

Conflict of Interest

The authors have no conflicts of interest directly relevant to the content of this article.


  1. 1.
    Cabinet Secretariat Website. Accessed 7 Mar 2018
  2. 2.
    Turner JR, Heiwitt JK, Morgan RK, Sims J, Carrol D, Kelly KA (1986) Graded mental arithmetic as an active psychological challenge. Int J Psychophysiol 3(4):307–309CrossRefGoogle Scholar
  3. 3.
    Sasaki T, Matsumoto S (2005) Actual conditions of work, fatigue and sleep in non-employed, home-based female information technology workers with preschool children. Ind Health 43:142–150CrossRefGoogle Scholar
  4. 4.
    Hsu C, Tsai M, Huang G, Lin T, Chen K, Ho S, Shyu L, Li Y (2012) Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction. Acta Anaesthesiol Taiwan 50:12–18CrossRefGoogle Scholar
  5. 5.
    Hoshi RA, Pastre CM, Vanderlei LCM, Godoy MF (2013) Poincaré plot indexes of heart rate variability: relationships with other nonlinear variables. Auton Neurosci Basic Clin 177:271–274CrossRefGoogle Scholar
  6. 6.
    Smith AJ, Owen H, Reynolds KJ (2013) Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox. J Clin Monit Comput 27:569–576. Scholar
  7. 7.
    Smith AJ, Owen H, Reynolds KJ (2013) Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation. J Clin Monit Comput 27:577–585. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chié Kurosaka
    • 1
    Email author
  • Hiroyuki Kuraoka
    • 1
  • Shimpei Yamada
    • 1
  • Shinji Miyake
    • 1
  1. 1.University of Occupational and Environmental Health, JapanKitakyushuJapan

Personalised recommendations