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Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method

  • Systems-Level Quality Improvement
  • Published:
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Abstract

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called “LLE”(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

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Authors and Affiliations

  1. School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    M. Mirsadeghi, H. Behnam & R. Shalbaf

  2. Department of Anesthesia, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    H. Jelveh Moghadam

Authors
  1. M. Mirsadeghi
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  2. H. Behnam
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  3. R. Shalbaf
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  4. H. Jelveh Moghadam
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Corresponding author

Correspondence to H. Behnam.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Mirsadeghi, M., Behnam, H., Shalbaf, R. et al. Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method. J Med Syst 40, 13 (2016). https://doi.org/10.1007/s10916-015-0382-4

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