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[Study of functional connectivity during anesthesia based on sparse partial least squares].

Fan Wu1, Zhongyi Jiang1, Hui Bi1

  • 1School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R.China;Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|June 30, 2020
PubMed
Summary

This study introduces Sparse Partial Least Squares (SPLS) for anesthesia consciousness monitoring. SPLS analysis of electroencephalogram data achieved 87.93% accuracy in distinguishing awake, moderate, and deep anesthesia states.

Keywords:
functional connectivitynetwork analysissparse partial least squaresstate of consciousness during anesthesiasynchronization likelihood

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Area of Science:

  • Neuroscience
  • Anesthesiology
  • Biomedical Engineering

Background:

  • Anesthesia consciousness monitoring is crucial for patient safety and neuroscience research.
  • Current methods require better indicators for precise clinical anesthesia monitoring.

Purpose of the Study:

  • To identify reliable indicators for monitoring clinical anesthesia states.
  • To evaluate the effectiveness of Sparse Partial Least Squares (SPLS) in analyzing brain functional connectivity for anesthesia monitoring.

Main Methods:

  • Collected 5-minute resting electroencephalogram (EEG) data from 14 patients in awake, moderate, and deep anesthesia states.
  • Utilized Sparse Partial Least Squares (SPLS) and Synchronized Likelihood (SL) to compute brain functional connectivity.
  • Employed Support Vector Machine (SVM) for classification of consciousness states based on connectivity features.

Main Results:

  • Both SPLS and SL methods showed similar trends in network parameters across different consciousness states.
  • SPLS-derived network parameters were statistically significant (P<0.05).
  • SVM classification using SPLS features achieved 87.93% accuracy, outperforming SL by 7.69%.

Conclusions:

  • Functional connectivity analysis using SPLS demonstrates superior performance in differentiating consciousness states during anesthesia.
  • The SPLS method offers a promising new approach for clinical anesthesia monitoring.