Discrimination of Vascular Conditions Using a Probabilistic Neural Network
Akira Sakane*, Toshio Tsuji*, Noboru Saeki**,
and Masashi Kawamoto**
*Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan
**Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan
This paper proposes a new method to discriminate vascular conditions from changes of biological signals and arterial wall impedance using a neural network. Since strong individual differences cause difficulty in discriminating the vascular conditions, we introduce an impedance ratio of the arterial wall and attempt to discriminate the vascular conditions during surgical operations. From experimental results, it is shown that various stimulations during operations cause changes in the impedance parameters of the arterial wall, and vascular conditions such as vasodilation, vasoconstriction, and shock can be discriminated accurately using the proposed method. This method will be useful for monitoring the vascular conditions during operations.
and Masashi Kawamoto, “Discrimination of Vascular Conditions Using a Probabilistic Neural Network,” J. Robot. Mechatron., Vol.16, No.2, pp. 138-145, 2004.
-  D. E. Longnecker and F. L. Murphy, “Introduction to Anesthesia,” Elsevier Science Health Science div, 1997.
-  H. Takada, S. M. Mirbod, and H. Iwata, “The relative vascular age derived from acceleration plethysmogram: A new attempt,” Jpn. J. Appl. Physiol., Vol.28, No.2, pp. 115-121, 1998.
-  Y. Maniwa, T. Iokibe, M. Koyama, M. Yamamoto, and S. Ohta, “The Application of Pulse Wave Chaos in Clinical Medicine,” in Proc. 17th FUZY System Symposium, Chiba, pp. 787-790, 2001.
-  F. A. Mussa-Ivaldi, N. Hogan, and E. Bizzi, “Neural, mechanical and geometrical factors sub-serving arm posture in humans,” Journal of Neuroscience, Vol.5, No.10, pp. 2732-2743, 1985.
-  T. Tsuji, P. G. Morasso, K. Goto, and K. Ito, “Human hand impedance characteristics during maintained posture,” Biol. cybern., Vol.72, No.6 pp. 475-485, 1995.
-  T.Tsuji and M.Kaneko, “Estimation and Modeling of Human Hand Impedance during Isometric Muscle Contraction,” in Proc. ASME., DSC-Vol. 58, pp. 575-582, Atlanta, 1996.
-  A. Mascaro, and H. Asada, “Photoplethysmograph Fingernail Sensors for Measuring Finger Forces Without Haptic Obstruction,” IEEE Trans. Robot. Automat., Vol.17, No.5, pp. 698-708, 2001.
-  N. Saeki, M. Kawamoto, and O. Yuge, “Quantitative view of peripheral circulation,” Critical Care Medicine, Vol.28, No.12, A62(suppl), 2000.
-  A. Sakane, T. Tsuji, Y. Tanaka, N. Saeki, and M. Kawamoto, “Estimating Arterial Wall Impedance using a Plethysmogram,” in Proc. 29th Annual Conference of the IEEE Industrial Electronics Society, pp. 580-585, USA, 2003.
-  M. Peltoranta and G. Pfurtscheller, “Neural network based classification of non-averabed event-related EEG responses,” Med and Biol. Eng. and Comput., Vol.32, pp. 189-196, 1994.
-  A. Hiraiwa, K. Shimohara, and Y. Tokunaga, “EMG pattern analysis and classification by neural network,” in Proc. IEEE International Conference on Syst., Man and Cybern., pp. 1113-1115, 1989.
-  A. Hiraiwa, U.Uchida and K.Shimohara, “EMG/EEG pattern recognition by neural networks,” in Proc. Eleventh European Meeting on Cybernetics and Systems Research, pp. 1383-1390, 1992.
-  D. Nishikawa, W. Yu, H. Yokoi, and Y. Kuhazu, “EMG Prosthetic Hand Controller Discriminating Ten Motions using Real-time Learning Method,” in Proc. 1999 IEEE/RSJ International Conference on Intelligent Robotics and Systems, pp. 1592-1597, 1999.
-  D. D. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing I, pp. 318-362, MIT Press, 1986.
-  T. Tsuji, H. Ichinobe, and M. Kaneko, “A Proposal of the Feedforward Neural Network Based on the Gaussian Mixture Model and the Log-Linear Model,” Trans. Institute of Electronics, Information and Communication Engineers, Vol. J77-D-II, No.10, pp. 2093-2100, 1994 (in Japanese).
-  T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko, “A Log-Linearized Gassian Mixture Network and Its Application to EEG Pattern Classification,” IEEE Trans. Syst., Man, Cybern-Part C, Appl. and Rev., Vol.29, No.1, pp. 60-72, 1999.
-  T. Tsuji, O. Fukuda, M. Murakami, and M. Kaneko, “An EMG Controlled Pointing Device Using a Neural Network,” Trans. of the Society of Instrument and Control Engineers, Vol.37, No.5, pp. 425-431, 2001 (in Japanese).
-  R. A. Day and A. L. Underwood, QUANTITATIVE ANALYSIS 4th Edition. Prentice-Hall, 1980.
-  N. Goldschlager and M. J. Goldman, Principles of Clinical Electrocardiography, Prentice Hall, 1989.
-  J. Archdeacon, Correlation and Regression Analysis, Univ of Wisconsin Pr., 1994.
-  C. Drott, G. Gothberg, and G. Claes, “Endoscopic transthoracic sympathectomy: An efficient and safe method for the treatment of hyperhidrosis,” Journal of the American Academy of Dermatology, Vol.33, pp. 78-81, 1995.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.
Copyright© 2004 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.