Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states

Pham Lam Vuong, Aamir Saeed Malik, Jose Bornot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called motifs which are based on the ranking values of subsequence time series. Permutation entropy (PE) has been developed to describe the relative occurrence of each of these motifs. However, PE has few limitations, mainly its inability to differentiate between distinct patterns of a certain motif, and its sensitivity to noise. To minimize those limitations, Weighted-Permutation Entropy (WPE) was proposed as a modification version of PE to improve complexity measuring for times series. This paper presents an approach by incorporating WPE into the analysis of different physiological states namely EEG time series. Three different EEG physiological states, eye-closed (EC), eye-open (EO), and visual oddball task (VOT) were included to examine ability of WPE to identify and discriminate different physiological states. The classification using WPE has achieved the results with accuracy of 87% between EC and EO states, and 83% between EO and VOT, respectively, using linear discrimination analysis. The results showed the potential of WPE to be a promising feature for nonlinear analysis in different physiological states of brain. It was also observed that WPE also could be used as marker for large artifact with low frequency such as eye-blink.

LanguageEnglish
Title of host publicationIECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences
Subtitle of host publication"Miri, Where Engineering in Medicine and Biology and Humanity Meet"
Pages979-984
Number of pages6
ISBN (Electronic)9781479940844
DOIs
Publication statusPublished - 1 Jan 2014
Event3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 - Kuala Lumpur, Malaysia
Duration: 8 Dec 201410 Dec 2014

Conference

Conference3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014
CountryMalaysia
CityKuala Lumpur
Period8/12/1410/12/14

Fingerprint

Time series
Entropy
Nonlinear analysis
Brain

Keywords

  • CPEI
  • EEG
  • eye-blink
  • PE
  • Weighted permutation entropy

Cite this

Vuong, P. L., Malik, A. S., & Bornot, J. (2014). Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states. In IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet" (pp. 979-984). [7047658] https://doi.org/10.1109/IECBES.2014.7047658
Vuong, Pham Lam ; Malik, Aamir Saeed ; Bornot, Jose. / Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states. IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet". 2014. pp. 979-984
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abstract = "An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called motifs which are based on the ranking values of subsequence time series. Permutation entropy (PE) has been developed to describe the relative occurrence of each of these motifs. However, PE has few limitations, mainly its inability to differentiate between distinct patterns of a certain motif, and its sensitivity to noise. To minimize those limitations, Weighted-Permutation Entropy (WPE) was proposed as a modification version of PE to improve complexity measuring for times series. This paper presents an approach by incorporating WPE into the analysis of different physiological states namely EEG time series. Three different EEG physiological states, eye-closed (EC), eye-open (EO), and visual oddball task (VOT) were included to examine ability of WPE to identify and discriminate different physiological states. The classification using WPE has achieved the results with accuracy of 87{\%} between EC and EO states, and 83{\%} between EO and VOT, respectively, using linear discrimination analysis. The results showed the potential of WPE to be a promising feature for nonlinear analysis in different physiological states of brain. It was also observed that WPE also could be used as marker for large artifact with low frequency such as eye-blink.",
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Vuong, PL, Malik, AS & Bornot, J 2014, Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states. in IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet"., 7047658, pp. 979-984, 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014, Kuala Lumpur, Malaysia, 8/12/14. https://doi.org/10.1109/IECBES.2014.7047658

Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states. / Vuong, Pham Lam; Malik, Aamir Saeed; Bornot, Jose.

IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet". 2014. p. 979-984 7047658.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Vuong PL, Malik AS, Bornot J. Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states. In IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet". 2014. p. 979-984. 7047658 https://doi.org/10.1109/IECBES.2014.7047658