NDLI: Comparison of Adaptive and Fixed Segmentation in Different Calculation Methods of Electroencephalogram Time-series Entropy for Estimating Depth of Anesthesia

Content Provider	IEEE Xplore Digital Library
Author	Ahmadi, B. Aimrfattahi, R. Negahbani, E. Mansouri, M. Taheri, M.
Copyright Year	2007
Description	Author affiliation: Isfahan Univ. of Technol., Isfahan (Ahmadi, B.)
Abstract	This paper proposes a combined method including adaptive segmentation and time-series Shannon entropy of electroencephalograms (EEG) to monitor depth of anesthesia (DOA). The entropy of a single channel EEG was computed through various methods of quantization. These methods are different in number of bins associated to the whole range of amplitude. The EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. Due to the non-stationary nature of EEG signal, adaptive segmentation methods seem to have better results. Our adaptive windowing methods consist of variance and auto correlation (ACF) based methods. We have compared the results of fixed and adaptive windowing in different methods of calculating entropy in order to estimate DOA. Coefficient of determination (R ) was used as a measure of correlation between the predictors and BIS index to evaluate our proposed methods. The results show that entropy decreases with decreasing DOA. In ICU, our proposed method reveals better performance than previous works. In both ICU and operating room, the results indicate the superiority of our method, especially applying adaptive segmentation. The mixture of adaptive windowing methods with different methods of calculating entropy would result in an outstanding performance.
Starting Page	265
Ending Page	268
File Size	2270074
Page Count	4
File Format	PDF
ISBN	9781424418671
DOI	10.1109/ITAB.2007.4407398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-08
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Anesthesia Electroencephalography Anesthetic drugs Data mining Computerized monitoring Direction of arrival estimation Patient monitoring Surgery Biomedical monitoring time-series Shannon entropy Adaptive segmentation Bispectral index depth of anesthesia fixed windowing
Content Type	Text
Resource Type	Article

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