Microcontroller Based Fuzzy Logic Sevofluorane Anesthesia Control System

Yardımcı, Ahmet; Ferikoğlu, Abdullah; Hadimioğlu, Necmiye

doi:10.1007/3-540-45493-4_18

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Bu çalışma ile birlikte farklı anestezi durumlarında elde edilen cerrahi veriler ile anestezik doz seviyelerini modellemişlerdir [7]. 2001 yılında A. Yardımcı ve arkadaşları, sevofluran anestezik maddesinin, anestezi derinliğini bozmadan optimum seviyede verilmesi için BM tabanlı bir mikrodenetleyici sistemi tasarlamışlardır [8]. M. Mahfouf ve arkadaşları 2003 yılında, izofluran anestezik maddesi kullanarak ameliyat esnasında hastaların ortalama arter basıncı (Mean Arterial Pressure-MAP) değerlerini esas almışlar ve anestezik derinliği ölçmek için bir BM tabanlı kontrol sistemi tasarlamışlardır [9].…”

Section: Introductionunclassified

Bulanık Mantık ve Yapay Bağışıklık Sistemi Temelli Anestezik Derinlik Karar Destek Uygulaması

Şatır

Yilmaz²

2019

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Cerrahi müdahalelerde, anestezi derinliğini uygun hale getirmek amacıyla hastaların sistolik arter basıncını (Systolic Arterial Pressure-SAP) ve kalp atım hızını (Heart Pulse Rate-HPR) kontrol etmek iyi bilinen bir ölçüm yöntemidir. Ameliyat boyunca, SAP ve HPR verilerine bakılarak hastaya verilebilecek anestezi miktarı (Anesthesia Output-AO) hesaplanabilmektedir. Bu çalışmada anestezistlere, SAP ve HPR verilerinden yola çıkarak, hastaya verilebilecek en uygun anestezi miktarını, Yapay Bağışıklık Sistemi (YBS) ve Bulanık Mantık (BM) yardımı ile hassas hesaplayan bir Anestezik Derinlik Karar Destek Sistemi (ADKDS) önerilmektedir. Çalışmada tasarlanan sistem, hastalardan elde edilen giriş değerleri SAP, HPR ve çıkış parametresi AO kullanılarak, üyelik fonksiyonlarının en uygun değerlerini elde etmek için, Klonal Seçim Algoritmasının (KSA) her adımında arama, hesaplama ve değerlendirme gerçekleştirmiştir. ADKDS'nin literatürdeki çalışmalara göre daha verimli tahminlerde bulunduğu ve daha iyi sonuçlar elde ettiği gözlemlenmiştir.

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Section: Introductionunclassified

Bulanık Mantık ve Yapay Bağışıklık Sistemi Temelli Anestezik Derinlik Karar Destek Uygulaması

Şatır

Yilmaz²

2019

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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“…Increase of EEG delta activity is evaluated as a finding of anesthetic depth [13]. A fuzzy logiccontrolled system has been designed for sevoflurane, the anesthetic agent, by means of using the blood pressure and pulse data taken from patient [14].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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In this study, power spectrum of the EEG data and the heartbeat data obtained from 250 patients has been applied to the designed Neural network system. A backpropagation artificial neural network has been developed which contains 53 nodes in the input layer, 27 nodes in the hidden and 1 node in the output layer. In the artificial neural network inputs, the power spectral density values corresponding 1-50 Hz frequency interval of the EEG slices which has 10 seconds of time interval, the ratio of the total of the PSD values of current EEG slice to the total PSD values of EEG slice of pre-anesthesia, the ratio of the total PSD values of the EEG data to the total PSD values of the previous EEG data, and the previous anaesthetic gas ratio values have been applied and the network has been educated. The designed neural network system has been tested by using 10 data set obtained from 4 different patients. In the anesthetic gas prediction according to the anesthesia level, successful results have been obtained with the designed system. The system has been able to correctly purposeful responses in average accuracy of 94% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.

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Estimation of Medicine Amount Used Anesthesia by an Artificial Neural Network

Güntürkün

2009

J Med Syst

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In this study, Elman's recurrent neural networks using Resilient Back Propagation (RP) algorithm and feed-forward neural networks using adaptive learning rate algorithm (gdx) have been compared 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. EEG data have been recorded by being sampled once in every 2 ms. 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. When Elman Resilient BP and feed-forward network are compared, it is observed that resilient back propagation algorithm has generated values which are quite close to the applied anesthesia amount compared to gdx which is an adaptive learning algorithm. 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.

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Microcontroller Based Fuzzy Logic Sevofluorane Anesthesia Control System

Cited by 4 publications

References 13 publications

Bulanık Mantık ve Yapay Bağışıklık Sistemi Temelli Anestezik Derinlik Karar Destek Uygulaması

Bulanık Mantık ve Yapay Bağışıklık Sistemi Temelli Anestezik Derinlik Karar Destek Uygulaması

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

Estimation of Medicine Amount Used Anesthesia by an Artificial Neural Network

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