Monitoring the depth of anesthesia using discrete wavelet transform and power spectral density

Nguyen-Ky, T. and Wen, Peng and Li, Yan (2009) Monitoring the depth of anesthesia using discrete wavelet transform and power spectral density. In: 4th International Conference on Rough Sets and Knowledge Technology (RSKT 2009), 14-16 Jul 2009, Gold Coast, Australia.

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Abstract

This method combines wavelet techniques and power spectral density to monitor the depth of anesthesia (DOA) based on simplified EEG signals. After decomposing electroencephalogram (EEG) signals, the power spectral
density is chosen as a feature function for coefficients of discrete wavelet transform. By computing the mean and standard deviation of the power spectral density values, we can classify the EEG signals to three classes, corresponding
with the BIS values of 0 to 40, 40 to 60, and 60 to 100. Finally, three linear functions are proposed to compute DOA values.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Series: Lecture Notes in Computer Science, v. 5589. Permanent restricted access to published version due to publisher copyright policy.
Depositing User: Dr Tai Nguyen
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 04 Aug 2011 04:50
Last Modified: 25 Aug 2016 04:52
Uncontrolled Keywords: depth of anesthesia; wavelet transform; power spectral density
Fields of Research : 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
11 Medical and Health Sciences > 1103 Clinical Sciences > 110301 Anaesthesiology
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: doi: 10.1007/978-3-642-02962-2_44
URI: http://eprints.usq.edu.au/id/eprint/18222

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