E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network

Authors:
Hamdi Melih Saraoğlu;Burçak Edin
Affiliations:
Department of Electrical---Electronics Engineering, Dumlupınar University, Kütahya, Turkey;Kütahya Evliya Çelebi State Hospital, Kütahya, Turkey
Venue:
Journal of Medical Systems
Year:
2007

Citing 1
Cited 5

Neural network design

Neural network design

Anesthetic Level Prediction Using a QCM Based E-Nose

Journal of Medical Systems
A new QCM based E-NOSE model using decay method

CSS'11 Proceedings of the 5th WSEAS international conference on Circuits, systems and signals
Singular Spectrum Analysis of Sleep EEG in Insomnia

Journal of Medical Systems
Artificial Apnea Classification with Quantitative Sleep EEG Synchronization

Journal of Medical Systems
Control of Sevoflurane Anesthetic Agent Via Neural Network Using Electroencephalogram Signals During Anesthesia

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, an E-Nose system was realized for the anesthetic dose level prediction. For this purpose, sevoflurane anesthetic agent was measured using the E-Nose system implemented with sensor array of quartz crystal microbalances (QCM). In surgeries, anesthetic agents are given to the patients with carrier gases of oxygen (O2) and nitrous oxide (N2O). Frequency changes on QCM sensors to the eight sevoflurane anesthetic dose levels were recorded via RS-232 serial port. A multilayer feed forward artificial neural network (MLNN) structure was used to provide the relationship between the frequency change and the anesthetic dose level. The MLNNs were trained with the measured data using Levenberg---Marquardt algorithm. Then, the trained MLNNs were tested with random data. The results have showed that, acceptable anesthetic dose level predictions have been obtained successfully.