Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Determining the amount of anesthetic medicine to be applied by using elman's recurrent neural networks via resilient back propagation
Journal of Medical Systems · Ağustos 2010
YÖKSİS Kayıtları
Determining the Amount of Anesthetic Medicine to Be Applied by Using Elman s Recurrent Neural Networks via Resilient Back Propagation
Journao of Medical Systems · 2009 SCI-Expanded 4 atıf
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Makale Bilgileri
ISSN01485598
Yayın TarihiAğustos 2010
Cilt / Sayfa34 · 493-497
Scopus ID2-s2.0-77956060457
Özet
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. © 2009 Springer Science+Business Media, LLC.
Yazarlar (1)
1
R. Güntürkün
Anahtar Kelimeler
Depth of anesthesia
EEG power spectrum
Elman recurrent neural networks
Resilient back propagation
Kurumlar
Dumlupinar Üniversitesi
Kutahya Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Medical Systems
Q1
SJR Skoru0,962
H-Index120
YayıncıSpringer New York
ÜlkeUnited States
Information Systems (Q1)
Medicine (miscellaneous) (Q1)
Health Informatics (Q2)
Health Information Management (Q2)
Metrikler
7
Atıf
1
Yazar
4
Anahtar Kelime