https://scholars.lib.ntu.edu.tw/handle/123456789/637720
標題: | Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience | 作者: | Jiang, George J A SHOU-ZEN FAN Abbod, Maysam F HUI-HSUN HUANG Lan, Jheng-Yan FENG-FANG TSAI Chang, Hung-Chi Yang, Yea-Wen Chuang, Fu-Lan Chiu, Yi-Fang Jen, Kuo-Kuang Wu, Jeng-Fu Shieh, Jiann-Shing |
公開日期: | 2015 | 卷: | 2015 | 來源出版物: | BioMed research international | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637720 | ISSN: | 23146133 | DOI: | 10.1155/2015/343478 |
顯示於: | 醫學系 |
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