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Electroencephalogram fractal dimension as a measure of depth of anesthesia

Electroencephalogram fractal dimension as a measure of depth of anesthesia
Electroencephalogram fractal dimension as a measure of depth of anesthesia
This paper proposes a combined method including adaptive segmentation and Higuchi fractal dimension (HFD) of electroencephalograms (EEG) to monitor depth of anesthesia (DOA). The EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. Due to the nonstationary nature of EEG signal, adaptive segmentation methods seem to have better results. The HFD of a single channel EEG was computed through adaptive windowing methods consist of adaptive variance and auto correlation function (ACF) based methods. We have compared the results of fixed and adaptive windowing in different methods of calculating HFD in order to estimate DOA. Prediction probability (Pk) was used as a measure of correlation between the predictors and BIS index to evaluate our proposed methods. The results show that HFD increases with increasing BIS index. In ICU, all of the methods reveal better performance than in other groups. In both ICU and operating room, the results indicate no obvious superiority in calculating HFD through adaptive segmentation.
adaptive segmentation, bispectral index, depth of anesthesia, fractal dimension
Negahbani, Ehsan
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Amirfattahi, Rasool
31c5f488-0276-400f-9e31-74e953f02d2a
Ahmadi, Behzad
8c2347f9-b79f-4beb-b8e9-3cbfabadd97f
Dehnavi, Alireza Mehri
8e820265-efa6-4d4b-85ad-9e09974ab8d9
Rouzbeh, Mohmmad
42460ac5-7d67-453d-9c31-5773cfe9ec0c
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a
Hashemi, Zeinab
caf16db8-79d8-4384-9c76-127a1321f889
Negahbani, Ehsan
42cc491f-0589-4961-b736-678c23c35b26
Amirfattahi, Rasool
31c5f488-0276-400f-9e31-74e953f02d2a
Ahmadi, Behzad
8c2347f9-b79f-4beb-b8e9-3cbfabadd97f
Dehnavi, Alireza Mehri
8e820265-efa6-4d4b-85ad-9e09974ab8d9
Rouzbeh, Mohmmad
42460ac5-7d67-453d-9c31-5773cfe9ec0c
Zaghari, Bahareh
a0537db6-0dce-49a2-8103-0f4599ab5f6a
Hashemi, Zeinab
caf16db8-79d8-4384-9c76-127a1321f889

Negahbani, Ehsan, Amirfattahi, Rasool, Ahmadi, Behzad, Dehnavi, Alireza Mehri, Rouzbeh, Mohmmad, Zaghari, Bahareh and Hashemi, Zeinab (2008) Electroencephalogram fractal dimension as a measure of depth of anesthesia. 3rd International Conference on Information & Communication Technologies: from Theory to Applications ( ICTTA2008), Damascus, Syrian Arab Republic. 06 - 10 Apr 2008.

Record type: Conference or Workshop Item (Other)

Abstract

This paper proposes a combined method including adaptive segmentation and Higuchi fractal dimension (HFD) of electroencephalograms (EEG) to monitor depth of anesthesia (DOA). The EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. Due to the nonstationary nature of EEG signal, adaptive segmentation methods seem to have better results. The HFD of a single channel EEG was computed through adaptive windowing methods consist of adaptive variance and auto correlation function (ACF) based methods. We have compared the results of fixed and adaptive windowing in different methods of calculating HFD in order to estimate DOA. Prediction probability (Pk) was used as a measure of correlation between the predictors and BIS index to evaluate our proposed methods. The results show that HFD increases with increasing BIS index. In ICU, all of the methods reveal better performance than in other groups. In both ICU and operating room, the results indicate no obvious superiority in calculating HFD through adaptive segmentation.

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More information

Published date: 9 April 2008
Venue - Dates: 3rd International Conference on Information & Communication Technologies: from Theory to Applications ( ICTTA2008), Damascus, Syrian Arab Republic, 2008-04-06 - 2008-04-10
Keywords: adaptive segmentation, bispectral index, depth of anesthesia, fractal dimension
Organisations: Dynamics Group

Identifiers

Local EPrints ID: 352998
URI: http://eprints.soton.ac.uk/id/eprint/352998
PURE UUID: c6541bbf-7f70-4f41-a48a-13982907d6d1

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Date deposited: 28 May 2013 13:44
Last modified: 11 Dec 2021 02:12

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Contributors

Author: Ehsan Negahbani
Author: Rasool Amirfattahi
Author: Behzad Ahmadi
Author: Alireza Mehri Dehnavi
Author: Mohmmad Rouzbeh
Author: Bahareh Zaghari
Author: Zeinab Hashemi

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