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Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network

Yıl 2020, Cilt: 2 Sayı: 2, 24 - 29, 26.10.2020

Öz

In this study, Elman recurrent neural network has been used in order to determine the depth of anesthesia in the continuation stage of anesthesia and to estimate the amount of medicine to be applied at that moment. The applied artificial network is composed of three layers, namely the input layer, the hidden layer and the output layer. Fast back-propagation learning algorithm (Traingdx) has been used in the training of the network, and nonlinear activation function sigmoid (sigmoid function) has been used in the outputs of the hidden layer and the output layer. The values of the power spectral density values of 10-second EEG(elektroensefalografi) segments which correspond to 1-50 Hz frequency range; the ratio of the total power of PSD(power spectral density) values of the EEG segment in that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia; similarly, the ratio of the total of PSD values of EEG data to the total of PSD values of the previous EEG data; and the amount of anesthetic medicine have been entered into the inputs of artificial neural network.

Kaynakça

  • [1]. Muthuswamy, J., Roy, R J., The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia, IEEE Transactions on Bio-Medical Engineering, Volume 46, Issue 3, Pages 291-299, March 1999 [2]. Aydın, A., Çömelekoğlu, Ü., Koçak, Z., Özge, A., Atıcı, Ş., Oral, U Trakeal Entübasyona Stres Yanıtına Remifentanilin Hemodinamik Etkisi: Kantitatif.,EEG Analizi ile Korelasyonu Klinik Psikofarmokoloji Bülteni 11:235-241, 2001
  • [3]. Isaac, PA, Rosen, M., Lower oesophageal con-tractility and detection of awareness during an-aesthesia. Br J Anaesth, 65:319, 1990
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  • [6]. Witte, H., Doering, A., Galicki, M., Dörschel, J., Krajca, V., Eiselt, M., Application of optimized pattern recognition units in EEG analysis: common optimization of preprocessing and weights of neural networks as well as structure optimization, Medinfo, Volume 8, Part 1, Pages 833-837, 1995
  • [7]. Huupponen, E., Himanen, SL., Värri, A., Hasan, J., Saastamoinen, A., Lehtokangas, M., Saarinen, J., Fuzzy detection of EEG alpha without amplitude thresholding, Artificial Intelligence in Medicine, Volume 24, Issue 2, , Pages 133-147, February 2002
  • [8]. Litscher G, Schwarz G. Is there paradoxical arousal reaction in the EEG subdelta range in patients during anesthesia? J Neurosurg Anesthesiol 11:49-52. 1999
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  • [10]. James, C J., Jones, RD., Bones, P J., Carroll, G J., Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages, Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, Volume 110, Issue 12, Pages 2049-2063, December 1999
  • [11]. Arıkan, MK. Psikiyatrik Elektrofizyoloji, Lilly İlaç A.Ş. Yayınları, s. 14-20. 1998.
  • [12]. Brent, R.P.: Fast Training Algorithms for Multi-layer Neural Nets, IEEE Transactions on Neural Networks 2 (1991) 346–354
  • [13]. Riedmiller, M, and Braun, H.: A Direct Adaptive Method for Faster backpropagation learning: The RPROP Algorithm, Proceedings of the IEEE Int. Conf. On Neural Networks, San Francisco, CA, March 28, 1993.
  • [14]. Brent, R.P.: Fast Training Algorithms for Multi-layer Neural Nets, IEEE Transactions on Neural Networks 2 (1991) 346–354
  • [15]. Tosun, M., “Inhalasyon anesthesia sevafloran rate Neuro-Fuzzy system with control“, University of Sakarya Institute of Science Phd thesis S. 114, 2004
  • [16]. Rüştü Güntürkün, “Using Elman Recurrent Neural Networks with Conjugate Gradient Algorithm in Determining the Anesthetic the Amount of Anesthetic Medicine to Be Applied”, J Med Syst (2010) 34:479–484, DOI 10.1007/s10916-009-9260-2
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Rüştü Güntürkün 0000-0002-6755-7941

Mustafa Tosun 0000-0001-7167-4561

Yayımlanma Tarihi 26 Ekim 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 2 Sayı: 2

Kaynak Göster

APA Güntürkün, R., & Tosun, M. (2020). Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 2(2), 24-29.
AMA Güntürkün R, Tosun M. Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network. Müh.Bil.ve Araş.Dergisi. Ekim 2020;2(2):24-29.
Chicago Güntürkün, Rüştü, ve Mustafa Tosun. “Estimation of the Amount of Drug to Be Applied to the Patient Using Elman Recurrent Artificial Neural Network”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 2, sy. 2 (Ekim 2020): 24-29.
EndNote Güntürkün R, Tosun M (01 Ekim 2020) Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network. Mühendislik Bilimleri ve Araştırmaları Dergisi 2 2 24–29.
IEEE R. Güntürkün ve M. Tosun, “Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network”, Müh.Bil.ve Araş.Dergisi, c. 2, sy. 2, ss. 24–29, 2020.
ISNAD Güntürkün, Rüştü - Tosun, Mustafa. “Estimation of the Amount of Drug to Be Applied to the Patient Using Elman Recurrent Artificial Neural Network”. Mühendislik Bilimleri ve Araştırmaları Dergisi 2/2 (Ekim 2020), 24-29.
JAMA Güntürkün R, Tosun M. Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network. Müh.Bil.ve Araş.Dergisi. 2020;2:24–29.
MLA Güntürkün, Rüştü ve Mustafa Tosun. “Estimation of the Amount of Drug to Be Applied to the Patient Using Elman Recurrent Artificial Neural Network”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, c. 2, sy. 2, 2020, ss. 24-29.
Vancouver Güntürkün R, Tosun M. Estimation of the Amount of Drug to be Applied to the Patient Using Elman Recurrent Artificial Neural Network. Müh.Bil.ve Araş.Dergisi. 2020;2(2):24-9.