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Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience
Joint Authors
Shieh, Jiann-Shing
Jiang, George J. A.
Huang, Hui-Hsun
Lan, Jheng-Yan
Tsai, Feng-Fang
Chang, Hung-Chi
Yang, Yea-Wen
Chuang, Fu-Lan
Chiu, Yi-Fang
Jen, Kuo-Kuang
Wu, Jeng-Fu
Abbod, Maysam F.
Fan, Shou-Zen
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-02-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jiang, George J. A.& Fan, Shou-Zen& Abbod, Maysam F.& Huang, Hui-Hsun& Lan, Jheng-Yan& Tsai, Feng-Fang…[et al.]. 2015. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience. BioMed Research International،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1055122
Modern Language Association (MLA)
Jiang, George J. A.…[et al.]. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience. BioMed Research International No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1055122
American Medical Association (AMA)
Jiang, George J. A.& Fan, Shou-Zen& Abbod, Maysam F.& Huang, Hui-Hsun& Lan, Jheng-Yan& Tsai, Feng-Fang…[et al.]. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1055122
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055122