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

George J.A. Jiang, Shou Zen Fan, Maysam F. Abbod, Hui Hsun Huang, Jheng Yan Lan, Feng Fang Tsai, Hung Chi Chang, Yea Wen Yang, Fu Lan Chuang, Yi Fang Chiu, Kuo Kuang Jen, Jeng Fu Wu, Jiann Shing Shieh

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

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.

Original languageEnglish
Article number343478
JournalBioMed Research International
Volume2015
DOIs
Publication statusPublished - Jan 1 2015

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

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