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 imp...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 9 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/8/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273346&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273346 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/8/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273346 109273346 109273346 10.1155/2015/343478 109273346 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience. aug: au: Jiang, George J. A. Fan, Shou-Zen Abbod, Maysam F. 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 Shieh, Jiann-Shing affil: Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan sug: subj: Anesthesia Neural Networks (Computer) Utilization Consciousness Human Funding Source Electroencephalography Male Female Adult Middle Age Aged Taiwan Academic Medical Centers ROC Curve Logistic Regression Multivariate Analysis Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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