Skip to main content

Advertisement

SpringerLink
Log in

Determining the Amount of Anesthetic Medicine to Be Applied by Using Elman’s Recurrent Neural Networks via Resilient Back Propagation

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this study, Elman recurrent neural networks have been defined by using Resilient Back Propagation in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. From 30 patients, 57 distinct EEG recordings have been collected prior to during anaesthesia of different levels. The applied artificial neural network is composed of three layers, namely the input layer, the middle layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. Prediction has been made by means of ANN. Training and testing the ANN have been used previous anaesthesia amount, total power/normal power and total power/previous. The system has been able to correctly purposeful responses in average accuracy of 95% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Isaac, P. A., and Rosen, M., Lower oesophageal con-tractility and detection of awareness during an-aesthesia. Br. J. Anaesth. 65:319, 1990. doi:10.1093/bja/65.3.319.

    Article  Google Scholar 

  2. Russell, I. F., Comparison of wakefulness with two anaesthetic regimens. Br. J. Anaesth. 58:965, 1986. doi:10.1093/bja/58.9.965.

    Article  Google Scholar 

  3. Muthuswamy, J., and Roy, R. J., The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia. IEEE Trans. Biomed. Eng. 46 (3)291–299, 1999.

    Article  Google Scholar 

  4. Aydın, A., Çömelekoğlu, Ü., Koçak, Z., Özge, A., Atıcı, Ş., Oral, U., The hemodynamic effect of remifentanil on stress response to tracheal, intubation: Correlations with quantitative EEG analysis. Bull. of Clin. Psychopharmacol. 11, N.: 4, 2001

  5. Da Silva, L., EEG analysis: theory and practice. In: Niedermayer, E., and Da Silva, L. (Eds.), Electroencephalography basic principles, clinical applications, and related fields, 4th edition. Lippincott Williams & Wilkins, USA, pp. 1135–58, 1999.

    Google Scholar 

  6. James, C. J., Jones, R. D., Bones, P. J., and Carroll, G. J., Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages. Clin. Neurophysiol. 110:2049–2063, 1999.

    Article  Google Scholar 

  7. Arıkan, M. K., Psychiatric electrophysiology, Lilly Medicine Stock Company Publication, pp. 14–20, 1998.

  8. Iselin-Chaves, I. A., Flaishon, R., Sebel, P. S., et al., The effect of the interaction of Propofol and Alfentanil on recall, loss of consciousness, and the bispectral index. Anesth. Analg. 87:949–955, 1998. doi:10.1097/00000539-199810000-00038.

    Article  Google Scholar 

  9. Gotman, S. Y., Pasupathy, J., Flanagan, A., Rosenblatt, D., and Gottesman, B., An expert system for EEG monitoring in the pediatric intensive care unit. Electroencephalogr. Clin. Neurophysiol. 106 (6)488–500, 1998.

    Article  Google Scholar 

  10. Ortolani, O., Conti, A., Di Filippo, A., Adembri, C., Moraldi, E., Evangelisti, A., Maggini, M., and Roberts, S. J., EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia. Br. J. Anaesth. 88(5):644–648, 2002. doi:10.1093/bja/88.5.644.

    Article  Google Scholar 

  11. Jiao, L., et al (eds.), Using back propagation feedback neural networks and recurrence quantification analysis of EEGs predict responses to incision during anesthesia, ICNC 2006, Part II, LNCS 4222, pp. 364–373, 2006.

  12. Temurtas, F., Gunturkun, R., Yumusak, N., and Temurtas, H., Harmonic detection using feed forward and recurrent neural networks for active filters. Electric Power. Syst. Res. 72:33–40, 2004.

    Article  Google Scholar 

  13. Riedmiller, M., and Braun, H., A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE Int. Conf. On Neural Networks, San Francisco, CA, 1993. March 28.

    Google Scholar 

  14. Brent, R. P., Fast training algorithms for multi-layer neural nets. IEEE Trans. Neural Netw. 2:346–354, 1991. doi:10.1109/72.97911.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Technical Education Faculty, Dumlupinar University, Simav, Kütahya, Turkey

    Rüştü Güntürkün

Authors
  1. Rüştü Güntürkün
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rüştü Güntürkün.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Güntürkün, R. Determining the Amount of Anesthetic Medicine to Be Applied by Using Elman’s Recurrent Neural Networks via Resilient Back Propagation. J Med Syst 34, 493–497 (2010). https://doi.org/10.1007/s10916-009-9262-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9262-0

Keywords

Search

Navigation