An Assessment Tool for Effective  Monitoring of Autonomic Nervous System Activity in Healthy People

Takuto Yanagida; Yoshimitsu Okita; Harunobu Nakamura; Toshifumi Sugiura; Hidenori Mimura

doi:10.20965/jaciii.2014.p0297

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JACIII Vol.18 No.3 pp. 297-304

doi: 10.20965/jaciii.2014.p0297

(2014)

Paper:

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An Assessment Tool for Effective Monitoring of Autonomic Nervous System Activity in Healthy People

Takuto Yanagida^, Yoshimitsu Okita^, Harunobu Nakamura^,
Toshifumi Sugiura^, and Hidenori Mimura^

^*Research Institute of Electronics, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu 432-8011, Japan

^**Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu 432-8011, Japan

^***Graduate School of Human Development and Environment, Kobe University, 3-11 Tsurukabuto, Nada-ku, Kobe 657-8501, Japan

Received:

August 23, 2013

Accepted:

March 14, 2014

Published:

May 20, 2014

Keywords:

lifestyle-related diseases, autonomic nervous system activity, electrocardiogram, plethysmogram, pulse transmission time

Abstract

This paper proposes an application that analyzes and displays electrocardiograms (ECG; electrical activity of the heart over time) and plethysmograms (PTG; pulse waves produced by the heart pumping blood to the periphery) measured simultaneously. Recently in developed countries, chronic conditions typified by lifestyle-related diseases have become the leading cause of death. Simplified monitoring of the condition can be an effective approach to disease prevention and health promotion. We have focused on autonomic nervous system activity (ANSA) because it responds to stress as well as to changes in dietary patterns, and is correlated with hypertension, the source of some diseases, such as coronary disease. In this paper, we deal with both ECGs and PTGs as part of the biological data that reflects ANSA. The proposed application enables doctors to seamlessly negotiate analyzed waveforms and index charts of ECGs and PTGs in sync with each other. It also helps them comprehend the transition of ANSA. It offers a user interface (UI) that enables doctors to observe the two measures and the relationship between them for a quick assessment of ANSA; the sonification function of the ECG indices is implemented for providing the multi-modality of the UI. An experiment was conducted to confirm the feasibility of the analysis method of the application.

Cite this article as:

T. Yanagida, Y. Okita, H. Nakamura, T. Sugiura, and H. Mimura, “An Assessment Tool for Effective Monitoring of Autonomic Nervous System Activity in Healthy People,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.3, pp. 297-304, 2014.

Data files:

References

[1] T. Takagi, M. Ohishi, N. Ito, M. Kaibe, Y. Tatara, M. Terai, A. Shiota, N. Hayashi, H. Rakugi, and T. Ogihara, “Evaluation of Morning Blood Pressure Elevation and Autonomic Nervous Activity in Hypertensive Patients UsingWavelet Transform of Heart Rate Variability,” Hypertension Research, Vol.29, No.12, pp. 977-987, 2006.
[2] L. A. Bortolotto, J. Blacher, T. Kondo, K. Takazawa, and M. E. Safar, “Assessment of Vascular Aging and Atherosclerosis in Hypertensive Subjects: Second Derivative of Photoplethysmogram Versus Pulse Wave Velocity,” American J. of Hypertension, Vol.13, No.2, pp. 165-171, 2000.
[3] A. Lorsheyd, D. d. Lange, M. Hijmering, M. Cramer, and A. v. d. Wiel, “PR and QTc interval prolongation on the electrocardiogram after binge drinking in healthy individuals,” The Netherlands J. of Medicine, Vol.63, No.2, pp. 59-63, 2005.
[4] J. P. Betancourt, C. Fatichah, M. L. Tangel, F. Yan, J. A. G. Sanchez, F.-Y. Dong, and K. Hirota, “Similarity-Based Fuzzy Classification of ECG and Capnogram Signals,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, pp. 302-310, 2013.
[5] Y. Kigawa and K. Oguri, “A Study for Accuracy Long Term ECG Detection Filter Using Support Vector Machine,” IEICE Trans. on Information and Systems, D (Japanese Edition), J89-D(6), pp. 1440-1448, 2006 (in Japanese).
[6] T. Kohama, S. Nakamura, and H. Hoshino, “An Efficient R-R Interval Detection for ECG Monitoring System,” IEICE Trans. on Information and Systems, E82-D(10), pp. 1425-1432, 1999.
[7] E. F. Treo, M. C. Herrera, and M. E. Valentinuzzi, “Algorithm for identifying and separating beats from arterial pulse records,” BioMedical Engineering OnLine, Vol.4, No.48 (online), 2005.
[8] K. Takazawa, N. Tanaka, M. Fujita, O. Matsuoka, T. Saiki, M. Aikawa, S. Tamura, and C. Ibukiyama, “Assessment of Vasoactive Agents and Vascular Aging by the Second Derivative of Photoplethysmogram Waveform,” Hypertension, Vol.32, pp. 365-370, 1998.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] T. Takagi, M. Ohishi, N. Ito, M. Kaibe, Y. Tatara, M. Terai, A. Shiota, N. Hayashi, H. Rakugi, and T. Ogihara, “Evaluation of Morning Blood Pressure Elevation and Autonomic Nervous Activity in Hypertensive Patients UsingWavelet Transform of Heart Rate Variability,” Hypertension Research, Vol.29, No.12, pp. 977-987, 2006.

[2] [2] L. A. Bortolotto, J. Blacher, T. Kondo, K. Takazawa, and M. E. Safar, “Assessment of Vascular Aging and Atherosclerosis in Hypertensive Subjects: Second Derivative of Photoplethysmogram Versus Pulse Wave Velocity,” American J. of Hypertension, Vol.13, No.2, pp. 165-171, 2000.

[3] [3] A. Lorsheyd, D. d. Lange, M. Hijmering, M. Cramer, and A. v. d. Wiel, “PR and QTc interval prolongation on the electrocardiogram after binge drinking in healthy individuals,” The Netherlands J. of Medicine, Vol.63, No.2, pp. 59-63, 2005.

[4] [4] J. P. Betancourt, C. Fatichah, M. L. Tangel, F. Yan, J. A. G. Sanchez, F.-Y. Dong, and K. Hirota, “Similarity-Based Fuzzy Classification of ECG and Capnogram Signals,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.17, No.2, pp. 302-310, 2013.

[5] [5] Y. Kigawa and K. Oguri, “A Study for Accuracy Long Term ECG Detection Filter Using Support Vector Machine,” IEICE Trans. on Information and Systems, D (Japanese Edition), J89-D(6), pp. 1440-1448, 2006 (in Japanese).

[6] [6] T. Kohama, S. Nakamura, and H. Hoshino, “An Efficient R-R Interval Detection for ECG Monitoring System,” IEICE Trans. on Information and Systems, E82-D(10), pp. 1425-1432, 1999.

[7] [7] E. F. Treo, M. C. Herrera, and M. E. Valentinuzzi, “Algorithm for identifying and separating beats from arterial pulse records,” BioMedical Engineering OnLine, Vol.4, No.48 (online), 2005.

[8] [8] K. Takazawa, N. Tanaka, M. Fujita, O. Matsuoka, T. Saiki, M. Aikawa, S. Tamura, and C. Ibukiyama, “Assessment of Vasoactive Agents and Vascular Aging by the Second Derivative of Photoplethysmogram Waveform,” Hypertension, Vol.32, pp. 365-370, 1998.

[9] [9] S. Watanabe and I. Yamaguchi (Eds.), “ECG Perfect Manual,” Yodosha, 2006 (in Japanese).

An Assessment Tool for Effective Monitoring of Autonomic Nervous System Activity in Healthy People

Takuto Yanagida*, Yoshimitsu Okita**, Harunobu Nakamura***, Toshifumi Sugiura*, and Hidenori Mimura*

Takuto Yanagida^, Yoshimitsu Okita^, Harunobu Nakamura^,
Toshifumi Sugiura^, and Hidenori Mimura^