Anesthetic Level Prediction Using a QCM Based E-Nose

Authors:
H. M. Saraoğlu;A. Özmen;M. A. Ebeoğlu
Affiliations:
Electrical and Electronics Engineering Dept., Dumlupinar University, Kütahya, Turkey 43100;Electrical and Electronics Engineering Dept., Dumlupinar University, Kütahya, Turkey 43100;Electrical and Electronics Engineering Dept., Dumlupinar University, Kütahya, Turkey 43100
Venue:
Journal of Medical Systems
Year:
2008

Citing 2
Cited 0

A Fuzzy Logic-Based Decision Support System on Anesthetic Depth Control for Helping Anesthetists in Surgeries

Journal of Medical Systems
E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network

Journal of Medical Systems

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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.