Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied
Journal of Medical Systems · Ağustos 2010
YÖKSİS Kayıtları
Using Elman Recurrent Neural Networks with Conjugate Gradient Algorithm in Determining the Anesthetic the Amount of Anesthetic Medicine to Be Applied
Journal of Medical Systems · 2010 SCI-Expanded
Prof. Dr. RÜŞTÜ GÜNTÜRKÜN →
Using Elman Recurrent Neural Networks with ConjugateGradient Algorithm in Determining the Anestheticthe Amount of Anesthetic Medicine to Be Applied
Journal of Medical Systems · 2010 SCI-Expanded
Prof. Dr. RÜŞTÜ GÜNTÜRKÜN →
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Using Elman Recurrent Neural Networks with Conjugate Gradient Algorithm in Determining the Anesthetic the Amount of Anesthetic Medicine to Be Applied
2010 ISSN: 0148-5598 SCI-Expanded
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Makale Bilgileri
ISSN01485598
Yayın TarihiAğustos 2010
Cilt / Sayfa34 · 479-484
Scopus ID2-s2.0-77956061801
Özet
In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm 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. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia. © 2009 Springer Science+Business Media, LLC.
Yazarlar (1)
1
R. Güntürkün
Anahtar Kelimeler
Conjugate gradient algorithm
Depth of anesthesia
EEG power spectrum
Elman recurrent neural networks
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