Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Journal: BioMed research international
Published Date: Feb 8, 2015

Abstract

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

Authors

  • George J A Jiang
    Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan.
  • Shou-Zen Fan
    Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
  • Maysam F Abbod
    College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK.
  • Hui-Hsun Huang
    Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
  • Jheng-Yan Lan
    Department of Anesthesiology, National Taiwan University Hospital, Yuan Lin Branch, Yuan Lin 64041, Taiwan.
  • Feng-Fang Tsai
    Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
  • Hung-Chi Chang
    Department of Anesthesiology, Shuang Ho Hospital, Taipei Medical University, Taipei 23561, Taiwan.
  • Yea-Wen Yang
    Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
  • Fu-Lan Chuang
    Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
  • Yi-Fang Chiu
    Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
  • Kuo-Kuang Jen
    Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, Longtan, Taoyuan 32500, Taiwan.
  • Jeng-Fu Wu
    Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, Longtan, Taoyuan 32500, Taiwan.
  • Jiann-Shing Shieh
    Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan.

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