Depth of anesthesia estimation by adaptive-network-based fuzzy inference system

Author

Zhang, Xu-Sheng ; Roy, Rob J.

Author_Institution

Dept. of Biomed. Eng., Rensselaer Polytech. Inst., Troy, NY, USA

Volume

1

fYear

1999

fDate

1999

Abstract

One effective way to estimate the depth of anesthesia (DOA) from EEG is proposed. The scheme applies an adaptive-network-based fuzzy inference system (ANFIS) to integrate the extracted EEG characteristics such as complexity measure, approximate entropy, and spectral edge frequency for decision-making. The system was trained and tested using EEG data collected from three dog experiments under propofol anesthesia. The accuracy of the system attains 89.5%. Comparison with artificial neural networks was made

Keywords

adaptive signal processing; electroencephalography; entropy; medical signal processing; patient monitoring; surgery; adaptive-network-based fuzzy inference system; approximate entropy; artificial neural networks; dog experiments; propofol anesthesia; spectral edge frequency; system accuracy; Anesthesia; Artificial neural networks; Data mining; Decision making; Direction of arrival estimation; Electroencephalography; Entropy; Frequency measurement; Fuzzy systems; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint

Conference_Location

Atlanta, GA

ISSN

1094-687X

Print_ISBN

0-7803-5674-8

Type

conf

DOI

10.1109/IEMBS.1999.802468

Filename

802468