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Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study

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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 neuro-fuzzy 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 high-validated 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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Abbreviations

BIS:

Bi-spectral Index signal

DOA:

Depth of anesthesia

IV:

Intravenous anesthesia

FFNN:

Feed forward neural network

ANN:

Artificial neural network

FFNN-L1:

Artificial neural network-1 hidden layer

FFNN-L2:

Artificial neural network-2 hidden layers

ANFIS:

Adaptive neuro-fuzzy inference systems

MLP:

Feed-forward multilayers perceptron

HR:

Heart rate

BP:

Blood pressure

PK:

Pharmacokinetics

PD:

Pharmacodynamics

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Acknowledgements

Many special thanks to Dr. Mohammad Dolati, Surgeon and Urology Specialist, and Dr. Fathi, Anesthesiologist and Academic Member of Mashhad University of Medical Sciences, for their professional help to prepare this paper.

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Authors and Affiliations

  1. Faculty of Industrial Engineering, Yazd University, Yazd, Iran

    Najmeh Jamali, Ahmad Sadegheih & M. M. Lotfi

  2. Department of Management, University of Otago, Dunedin, New Zealand

    Lincoln C. Wood

  3. Department of Mathematics, Chabahar Maritime University, Chabahar, Iran

    M. J. Ebadi

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  1. Najmeh Jamali
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  2. Ahmad Sadegheih
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  3. M. M. Lotfi
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  4. Lincoln C. Wood
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Correspondence to Ahmad Sadegheih.

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Jamali, N., Sadegheih, A., Lotfi, M.M. et al. Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study. Neural Process Lett 53, 131–175 (2021). https://doi.org/10.1007/s11063-020-10369-7

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