Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study
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Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study Najmeh Jamali1 · Ahmad Sadegheih1 · M. M. Lotfi1 · Lincoln C. Wood2 · M. J. Ebadi3 Accepted: 4 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This study aims to estimate the depth of anesthesia (DOA) at a safe and appropriate level taking into account the patient characteristics during the induction phase. Bi-spectral Index signal (BIS) as a common approach of controlling DOA generates noise and delays in the initial phase of induction. This may lead to useless information in the process of controlling. Moreover, using the BIS index entails a time-consuming process, high equipping costs, and a lack of accessibility to device accessories. To overcome these problems, we propose a new model of controlling DOA with no need for the use of such an index. Hence, an estimation strategy for DOA is developed applying a feedforward neural network and an adaptive neurofuzzy inference estimation model. This model estimates the dose of intravenous anesthetic drugs concerning the patients’ needs resulting in optimal drug dose and stable anesthesia depth. The proposed estimations are tested by sensitivity analysis being compared with real data obtained from the classical model (PK-PD) revised approach and BIS approach on 13 patients undergoing surgery. The results show an accuracy of 0.999, indicative of a highvalidated model. Compared to BIS, our proposed model not only controls DOA accurately but also achieves outcomes in practice successfully. Some practical implications for future research and clinical practice are also suggested.
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Ahmad Sadegheih [email protected] Najmeh Jamali [email protected] M. M. Lotfi [email protected] Lincoln C. Wood [email protected] M. J. Ebadi [email protected]
1
Faculty of Industrial Engineering, Yazd University, Yazd, Iran
2
Department of Management, University of Otago, Dunedin, New Zealand
3
Department of Mathematics, Chabahar Maritime University, Chabahar, Iran
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N. Jamali et al.
Keywords Induction phase · Anesthesia Bi-spectral Index signal · Intravenous anesthesia · Artificial neural network · Neuro-fuzzy
Abbreviation BIS DOA IV FFNN ANN FFNN-L1 FFNN-L2 ANFIS MLP HR BP PK PD
Bi-spectral Index signal Depth of anesthesia Intravenous anesthesia Feed forward neural network Artificial neural network Artificial neural network-1 hidden layer Artificial neural network-2 hidden layers Adaptive neuro-fuzzy inference systems Feed-forward multilayers perceptron Heart rate Blood pressure Pharmacokinetics Pharmacodynamics
1 Introduction Using a variety of infusion devices and administering a composition of anesthesia drugs to maintain patient physiology parameters during surgery, anesthesiologists aim to obtain an appropriate equilibrium between the patient analgesia and muscle relaxation. Assessing a composition of hypnosis and analgesia to obtain optimal sedation has become an integral element of critical care medi
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