Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform

Nusrat Ferdous; Md. Adnan Kiber

doi:doi:10.11648/j.ijiis.20140304.12

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Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform

Nusrat Ferdous, Md. Adnan Kiber

Published in International Journal of Intelligent Information Systems (Volume 3, Issue 4)

Received: 29 September 2014 Accepted: 14 October 2014 Published: 30 October 2014

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Abstract

Electroencephalogram (EEG) is the brain signal containing valuable information about the conscious and unconscious states of the brain, which may provide a useful tool to measure depth of anesthesia. However, raw EEG signals received in various states of consciousness cannot be distinguished visually. In this paper an approach is presented to find out difference between EEG signals in fully awake and in deep sleep conditions with respect to the coefficients of wavelet transform. Continuous wavelet transform of the raw EEG signal obtained at different conscious state of a human subject have been performed. Statistical analyses were then performed on coefficient values to determine the differences between the sleep state and the awake state. From statistical t-test analysis significant difference of the two state of consciousness was found.

Published in	International Journal of Intelligent Information Systems (Volume 3, Issue 4)
DOI	10.11648/j.ijiis.20140304.12
Page(s)	40-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Anesthesia, EEG, Wavelet Transform, T-Test

References

[1]	Bojan Musizza and Samo Ribaric, “Monitoring the Depth of Anaesthesia”, Sensors 2010, 10, 10896-10935; doi:10.3390/s101210896
[2]	Charles N. Horton, MD, “Anesthesia Crash Course”, Oxford University Press, 2009.
[3]	Tatjana Zikov, Stéphane Bibian, Guy A. Dumont, Mihai Huzmezan, and Craig R. Ries, “Quantifying Cortical Activity During General Anesthesia Using Wavelet Analysis”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL 2006.
[4]	M. F. Bear, B. W. Connors, and M. A. Paradiso, Neuroscience. Exploring the Brain. Baltimore: Williams & Wilkins, 1996.
[5]	E. Niedermayer, "The normal EEG of the waking adult," in Electroencephalography. Basic Principles, Clinical Applications and Related Fields (E. Niedermayer and F. Lopes da Silva, eds.), ch. 13, pp. 149-173, Baltimore: Williams & Wilkins, 1999.
[6]	C. Andrew and G. Pfurtscheller, "Event-related coherence as a tool for dynamic interaction of brain regions," Electroencephal. Clin. Neurophysiol., vol. 98, pp.144-148, 1996.
[7]	Tasneem Rahman, Khadija Akhter Suchi and K. Siddique-e-Rabbani, “Towards development of an anaesthesia monitoring device based on EEG”, conference proceeding, Recent Advances in Physics (RAP) 2010, March.
[8]	Paul S. Addison, “The Illustrated Wavelet Transform Handbook”, Bristol and Philadelphia, Institute of Physics Publishing, 2002.
[9]	Ingrid Daubechies, “Ten Lectures on Wavelets”, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1992
[10]	Leif Sornmo, Pablo Laguna, “ Bioelectrical Signal Processing in Cardiac and Neurological Applications”, Elsevier Academic Press. [4] M. F. Bear, B. W. Connors, and M. A. Paradiso, Neuroscience. Exploring the Brain.Baltimore: Williams & Wilkins, 1996.

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APA Style

Nusrat Ferdous, Md. Adnan Kiber. (2014). Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform. International Journal of Intelligent Information Systems, 3(4), 40-44. https://doi.org/10.11648/j.ijiis.20140304.12

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ACS Style

Nusrat Ferdous; Md. Adnan Kiber. Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform. Int. J. Intell. Inf. Syst. 2014, 3(4), 40-44. doi: 10.11648/j.ijiis.20140304.12

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AMA Style

Nusrat Ferdous, Md. Adnan Kiber. Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform. Int J Intell Inf Syst. 2014;3(4):40-44. doi: 10.11648/j.ijiis.20140304.12

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@article{10.11648/j.ijiis.20140304.12,
  author = {Nusrat Ferdous and Md. Adnan Kiber},
  title = {Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform},
  journal = {International Journal of Intelligent Information Systems},
  volume = {3},
  number = {4},
  pages = {40-44},
  doi = {10.11648/j.ijiis.20140304.12},
  url = {https://doi.org/10.11648/j.ijiis.20140304.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20140304.12},
  abstract = {Electroencephalogram (EEG) is the brain signal containing valuable information about the conscious and unconscious states of the brain, which may provide a useful tool to measure depth of anesthesia. However, raw EEG signals received in various states of consciousness cannot be distinguished visually. In this paper an approach is presented to find out difference between EEG signals in fully awake and in deep sleep conditions with respect to the coefficients of wavelet transform. Continuous wavelet transform of the raw EEG signal obtained at different conscious state of a human subject have been performed. Statistical analyses were then performed on coefficient values to determine the differences between the sleep state and the awake state. From statistical t-test analysis significant difference of the two state of consciousness was found.},
 year = {2014}
}

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TY  - JOUR
T1  - Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform
AU  - Nusrat Ferdous
AU  - Md. Adnan Kiber
Y1  - 2014/10/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ijiis.20140304.12
DO  - 10.11648/j.ijiis.20140304.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 40
EP  - 44
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20140304.12
AB  - Electroencephalogram (EEG) is the brain signal containing valuable information about the conscious and unconscious states of the brain, which may provide a useful tool to measure depth of anesthesia. However, raw EEG signals received in various states of consciousness cannot be distinguished visually. In this paper an approach is presented to find out difference between EEG signals in fully awake and in deep sleep conditions with respect to the coefficients of wavelet transform. Continuous wavelet transform of the raw EEG signal obtained at different conscious state of a human subject have been performed. Statistical analyses were then performed on coefficient values to determine the differences between the sleep state and the awake state. From statistical t-test analysis significant difference of the two state of consciousness was found.
VL  - 3
IS  - 4
ER  -

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Author Information

Nusrat Ferdous

Department of Electronics and Communication Engineering, Institute of Science and Technology, Dhaka, Bangladesh
Md. Adnan Kiber

Department of Applied Physics, Electronics and Communication Engineering, University of Dhaka, Dhaka, Bangladesh

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APA Style

Nusrat Ferdous, Md. Adnan Kiber. (2014). Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform. International Journal of Intelligent Information Systems, 3(4), 40-44. https://doi.org/10.11648/j.ijiis.20140304.12

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ACS Style

Nusrat Ferdous; Md. Adnan Kiber. Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform. Int. J. Intell. Inf. Syst. 2014, 3(4), 40-44. doi: 10.11648/j.ijiis.20140304.12

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AMA Style

Nusrat Ferdous, Md. Adnan Kiber. Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform. Int J Intell Inf Syst. 2014;3(4):40-44. doi: 10.11648/j.ijiis.20140304.12

Copy | Download

@article{10.11648/j.ijiis.20140304.12,
  author = {Nusrat Ferdous and Md. Adnan Kiber},
  title = {Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform},
  journal = {International Journal of Intelligent Information Systems},
  volume = {3},
  number = {4},
  pages = {40-44},
  doi = {10.11648/j.ijiis.20140304.12},
  url = {https://doi.org/10.11648/j.ijiis.20140304.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20140304.12},
  abstract = {Electroencephalogram (EEG) is the brain signal containing valuable information about the conscious and unconscious states of the brain, which may provide a useful tool to measure depth of anesthesia. However, raw EEG signals received in various states of consciousness cannot be distinguished visually. In this paper an approach is presented to find out difference between EEG signals in fully awake and in deep sleep conditions with respect to the coefficients of wavelet transform. Continuous wavelet transform of the raw EEG signal obtained at different conscious state of a human subject have been performed. Statistical analyses were then performed on coefficient values to determine the differences between the sleep state and the awake state. From statistical t-test analysis significant difference of the two state of consciousness was found.},
 year = {2014}
}

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TY  - JOUR
T1  - Estimating Depth of Anesthesia from EEG Signals Using Wavelet Transform
AU  - Nusrat Ferdous
AU  - Md. Adnan Kiber
Y1  - 2014/10/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ijiis.20140304.12
DO  - 10.11648/j.ijiis.20140304.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 40
EP  - 44
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20140304.12
AB  - Electroencephalogram (EEG) is the brain signal containing valuable information about the conscious and unconscious states of the brain, which may provide a useful tool to measure depth of anesthesia. However, raw EEG signals received in various states of consciousness cannot be distinguished visually. In this paper an approach is presented to find out difference between EEG signals in fully awake and in deep sleep conditions with respect to the coefficients of wavelet transform. Continuous wavelet transform of the raw EEG signal obtained at different conscious state of a human subject have been performed. Statistical analyses were then performed on coefficient values to determine the differences between the sleep state and the awake state. From statistical t-test analysis significant difference of the two state of consciousness was found.
VL  - 3
IS  - 4
ER  -

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