Skip to main content
Log in

Anesthetic Level Prediction Using a QCM Based E-Nose

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

Abstract

Anesthetic level measurement is a real time process. This paper presents a new method to measure anesthesia level in surgery rooms at hospitals using a QCM based E-Nose. The E-Nose system contains an array of eight different coated QCM sensors. In this work, the best linear reacting sensor is selected from the array and used in the experiments. Then, the sensor response time was observed about 15 min using classic method, which is impractical for on-line anesthetic level detection during a surgery. Later, the sensor transition data is analyzed to reach a decision earlier than the classical method. As a result, it is found out that the slope of transition data gives valuable information to predict the anesthetic level. With this new method, we achieved to find correct anesthetic levels within 100 s.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Marshall, B. E., and Lockenfger, D. E., General anesthetics. In: Brunton, L., Lazo, J., and Park, K. (Eds.), Goodman and Gilman’s The Pharmacological Basis of Therapeutics, 8th edition. Pergamon, Oxford, UK, pp. 285–311, 1990.

    Google Scholar 

  2. Mahfouf, M., Asbury, A. J., and Linkens, D. A., Unconstrained and constrained generalized predictive control of depth of anesthesia during surgery. Control Eng. Prac. 11:1501–1515, 2003.

    Article  Google Scholar 

  3. Becker, K., Thull, B., Kasmacher-Leidinger, H., Stemmer, J., Rau, G., Kalf, G., and Zimmermann, H., Design and validation of an intelligent patient monitoring and alarm system based on fuzzy logic process model. Art. Int. Med. 11:33–53, 1997.

    Article  Google Scholar 

  4. Vefghi, L., and Linkens, D. A., Internal representation in neural networks used for classification of patient anesthetic states and dosage. Comput. Methods Programs Biomed. 59:75–89, 1999.

    Article  Google Scholar 

  5. Saraoğlu, H. M., Şanlı, S., Fuzzy Logic based anesthetic depth control. In: 2003 ICIS International Conference on Signal Processing, Çanakkale, Turkey, pp. 24–26, 2003.

  6. Saraoğlu, H. M., and Edin, B., E-Nose system for anesthetic dose level detection using artificial neural network. J. Med. Syst. 6:475–482, 2007.

    Article  Google Scholar 

  7. Saraoğlu, H. M., and Şanlı, S., A fuzzy logic-based decision support system on anesthetic depth control for helping anesthetists in surgeries. J. Med. Syst. 6:511–519, 2007.

    Article  Google Scholar 

  8. Başova, T. V., Taşaltın, C., Gürek, A. G., Öztürk, Z. Z., and Ahsen, V., Mesomorphic phthalocyanine as chemically sensitive coatings for chemical sensors. Sens. Actuators B. 96:70–75, 2003.

    Article  Google Scholar 

  9. Özmen, A., Ebeoğlu, M. A., Tekce, F., Taşaltın, C., and Öztürk, Z. Z., Finding the composition of gas mixtures by a phthalocyanine coated QCM sensor array and an artificial neural network. Sens. Actuators B. 115:1450–454, 2006.

    Article  Google Scholar 

  10. King, H. W., Piezoelectric sorption detector. Anal. Chem. 36:1735–1739, 1964.

    Article  Google Scholar 

  11. Temurtas, F., Fast detection of hazardous organic gases in the ambient air using adaptive neuro-fuzzy inference systems. Int. J. Environ. Pollut. 28:3/4352–363, 2006.

    Article  Google Scholar 

  12. Gulbag, A., and Temurtas, F., A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures. Sens. Actuators B. 121:2590–599, 2007.

    Article  Google Scholar 

Download references

Acknowledgment

This research is supported by TUBITAK (104E053) and DPU (2004-6). The QCM sensors were developed and provided by Department of Materials and Chemical Technologies, Marmara Research Center, TUBITAK. Evliya Çelebi State Hospital-Kütahya administration allowed us to conduct the experiments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Özmen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saraoğlu, H.M., Özmen, A. & Ebeoğlu, M.A. Anesthetic Level Prediction Using a QCM Based E-Nose. J Med Syst 32, 251–257 (2008). https://doi.org/10.1007/s10916-008-9130-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-008-9130-3

Keywords

Navigation