Estimating Depth of Anesthesia with Sparsity Measure of EEG Data in Wavelet Domain

Author

Duan Li ; Liang, Zhenhu ; Xiaoli Li

Author_Institution

Inst. of Inf. Sci. & Eng., Yanshan Univ., Qinhuangdao, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

5

Abstract

Monitoring the effect of anesthetic drug on the central nervous system is challenging in the surgery. Several methods based on the electroencephalogram (EEG) have been proposed to estimate the depth of anesthesia (DOA). In this paper, a novel method is proposed to estimate the DOA with the sparsity measure of EEG data. The performance of the new DOA measure is assessed by pharmacokinetic/pharmacodynamic (PKPD) modeling and prediction probability analysis. The test of 17 cases shows this measure may efficiently track the effect of the sevoflurane on the brain activity.

Keywords

drugs; electroencephalography; medical signal processing; prediction theory; probability; wavelet transforms; EEG; anesthetic drug; brain activity; central nervous system; depth of anesthesia; electroencephalogram; pharmacodynamic modeling; pharmacokinetic modeling; prediction probability analysis; sevoflurane; sparsity measure; wavelet transform; Anesthesia; Anesthetic drugs; Brain modeling; Central nervous system; Direction of arrival estimation; Electroencephalography; Monitoring; Predictive models; Surgery; Wavelet domain;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4132-7

Electronic_ISBN

978-1-4244-4134-1

Type

conf

DOI

10.1109/BMEI.2009.5305340

Filename

5305340