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Journals / Mühendislik Bilimleri ve Araştırmaları Dergisi / 2020 / Cilt: 2 Sayı: 2

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

Rüştü Güntürkün(Dumlupınar Üniversitesi)0000-0002-6755-7941
Mustafa Tosun(Dumlupınar Üniversitesi)0000-0001-7167-4561
Pages
24–29
Publication Date
2020-10-26
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:The applied artificial network is composed of three layers, namely the input layer, the hidden layer and the output layer.
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Conclusion & Impact: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.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Depth of anesthesia · Neuro Control · EEG power spectrum

Özet

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.

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Cite This Article

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, Cilt: 2 Sayı: 2, 24–29. https://ajindex.com/articles/1531426

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