Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.
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.
Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan.
George J A
GJ
Jiang
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Shou-Zen
SZ
Fan
Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK.
Maysam F
MF
Abbod
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Hui-Hsun
HH
Huang
Department of Anesthesiology, National Taiwan University Hospital, Yuan Lin Branch, Yuan Lin 64041, Taiwan.
Jheng-Yan
JY
Lan
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Feng-Fang
FF
Tsai
Department of Anesthesiology, Shuang Ho Hospital, Taipei Medical University, Taipei 23561, Taiwan.
Hung-Chi
HC
Chang
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Yea-Wen
YW
Yang
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Fu-Lan
FL
Chuang
Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
Yi-Fang
YF
Chiu
Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, Longtan, Taoyuan 32500, Taiwan.
Kuo-Kuang
KK
Jen
Missile & Rocket Systems Research Division, National Chung-Shan Institute of Science and Technology, Longtan, Taoyuan 32500, Taiwan.
Jeng-Fu
JF
Wu
Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan ; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan.
Jiann-Shing
JS
Shieh
D000328:Adult; D000368:Aged; D000758:Anesthesia; D003243:Consciousness; D004569:Electroencephalography; D019277:Entropy; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted