Wavelet entropy analysis for ictal electroencephalogram signals of child absence epilepsy_Journal of Biomedical Engineering

Authors：

 ZHANG Meiyun ¹ , WANG Chen ¹ , ZHANG Ying ¹ , CHEN Ying ² , WU Bo ³ , ZHANG Yuqin ³ , WANG Fenglou ²

1. Neurology Department of Tianjin Medical Union Center, Tianjin 300121, P.R.China;
2. Neurology Department of Tianjin Medical University General Hospital, Tianjin 300051, P.R.China;
3. Neurology Department of Tianjin Children Hospital, Tianjin 300074, P.R.China;

Corresponding author：

ZHANG Meiyun, Email: zmy22202@aliyun.com

Keywords：

child absence epilepsy; electroencephalogram; wavelet entropy

DOI：

10.7507/1001-5515.201701002

Video：

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The integral and individual-scale wavelet entropy of electroencephalogram (EEG) were employed to investigate the information complexity in EEG and to explore the dynamic mechanism of child absence epilepsy (CAE). The digital EEG signals were collected from patients with CAE and normal controls. Time-frequency features were extracted by continuous wavelet transformation. Individual scale power spectrum characteristics were represented by wavelet-transform. The integral and individual-scale wavelet entropy of EEG were computed on the basis of individual scale power spectrum. The evolutions of wavelet entropy across ictal EEG of CAE were investigated and compared with normal controls. The integral wavelet entropy of ictal EEG is lower than inter-ictal EEG for CAE, and it also lower than normal controls. The individual-scale wavelet entropies of 12th scale (centered at 3 Hz) of ictal EEG in CAE was significantly higher than normal controls. The individual-scale wavelet entropies for α band (centered at 10 Hz) of ictal EEG in CAE were much lower than normal controls. The integral wavelet entropy of EEG can be considered as a quantitative parameter of complexity for EEG signals. The complexity of ictal EEG for CAE is obviously declined in CAE. The wavelet entropies declined could become quantitative electrophysiological parameters for epileptic seizures, and it also could provide a theoretical basis for the study of neuromodulation techniques in epileptic seizures.

Citation： ZHANG Meiyun, WANG Chen, ZHANG Ying, CHEN Ying, WU Bo, ZHANG Yuqin, WANG Fenglou. Wavelet entropy analysis for ictal electroencephalogram signals of child absence epilepsy. Journal of Biomedical Engineering, 2018, 35(4): 530-538. doi: 10.7507/1001-5515.201701002 Copy

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8.	Liu Quan, Chen Yifeng, Fan Shouzen, et al. EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery. Med Biol Eng Comput, 2017, 55(8): 1435-1450.
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11.	Yordanova J, Kolev V, Rosso O A, et al. Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance. J Neurosci Methods, 2002, 117(1): 99-109.
12.	Rosso O A. Entropy changes in brain function. Int J Psychophysiol, 2007, 64(1): 75-80.
13.	Emre C M, Ozgoren M, Acar S F. Continuous time wavelet entropy of auditory evoked potentials. Comput Biol Med, 2010, 40: 90-96.
14.	Blanco S, Figliola A, Quiroga R Q, et al. Time-frequency analysis of electroencephalogram series. Ⅲ. Wavelet packets and information cost function. Physical Review E, 1998, 57(1): 932-940.
15.	Shannon C E. A mathematical theory of communication. Bell Syst Tech J, 1948, 27(3): 379-656.
16.	Feldman D P, Crutchfield J P. Measures of statistical complexity: why? Phys Lett A, 1998, 238(4-5): 244-252.
17.	Mitschake F, Möller M, Large W. Measuring filtered chaotic signals. Phys Rev A, 1988, 37(11): 4518-4521.
18.	Rosso O A, Hyslop W, Gerlach R, et al. Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy. Physica A, 2005, 356(1): 184-189.
19.	Rosso O A, Martin M T, Figliola A, et al. EEG analysis using wavelet-based information tools. J Neurosci Methods, 2006, 153(2): 163-182.
20.	Pereyra M E, Lamberti P W, Rosso O A. Wavelet Jensen-Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures. Physica A, 2007, 379(1): 122-132.
21.	Acharya U R, Molinari F, Sree S, et al. Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control, 2012, 7(4): 401-408.
22.	Wang Deng, Miao Duoqian, Xie Chen. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl, 2011, 38(11): 14314-14320.
23.	Rosso O A, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods, 2001, 105(1): 65-75.

1. Powell C E, Perceival I C. A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems. J Phys A, 1979, 12: 2053-2071.
2. Inouye T, Shinosaki K, Iyama A, et al. Localization of activated areas and directional EEG patterns during mental arithmetic. Clin Neurophysiol, 1993, 86(4): 224-230.
3. Stam C J, Tavy D L J, Keunen R W M. Quantification of alpha rhythm desynchronization using the acceleration spectrum entropy of the EEG. Clin Electroencephalogr, 1993, 24(3): 104-109.
4. Inouye T, Shinosaki K, Sakamoto H, et al. Quantification of EEG irregularity by use of the entropy of power spectrum. Electroencephalogr Clin Neurophysiol, 1991, 79(3): 204-210.
5. Yildiz A, Akin M, Poyraz M, et al. Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst Appl, 2009, 36(4): 7390-7399.
6. Thanaraj P, Parvathavarthini B. Multichannel interictal spike activity detection using time-frequency entropy measure. Australas Phys Eng Sci Med, 2017, 40(2): 413-425.
7. 郁洪强, 赵欣, 詹启生, 等. 基于小波熵的网络成瘾脑电复杂性分析. 天津大学学报, 2008, 41(6): 751-756.
8. Liu Quan, Chen Yifeng, Fan Shouzen, et al. EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery. Med Biol Eng Comput, 2017, 55(8): 1435-1450.
9. 王锋, 李晓欧. 基于脑电信号的麻醉特征参数分析. 生物医学工程学杂志, 2015, 32(1): 13-18, 31.
10. Quiroga R Q, Rosso O A, Basar E, et al. Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations. Biol Cybern, 2001, 84(4): 291-299.
11. Yordanova J, Kolev V, Rosso O A, et al. Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance. J Neurosci Methods, 2002, 117(1): 99-109.
12. Rosso O A. Entropy changes in brain function. Int J Psychophysiol, 2007, 64(1): 75-80.
13. Emre C M, Ozgoren M, Acar S F. Continuous time wavelet entropy of auditory evoked potentials. Comput Biol Med, 2010, 40: 90-96.
14. Blanco S, Figliola A, Quiroga R Q, et al. Time-frequency analysis of electroencephalogram series. Ⅲ. Wavelet packets and information cost function. Physical Review E, 1998, 57(1): 932-940.
15. Shannon C E. A mathematical theory of communication. Bell Syst Tech J, 1948, 27(3): 379-656.
16. Feldman D P, Crutchfield J P. Measures of statistical complexity: why? Phys Lett A, 1998, 238(4-5): 244-252.
17. Mitschake F, Möller M, Large W. Measuring filtered chaotic signals. Phys Rev A, 1988, 37(11): 4518-4521.
18. Rosso O A, Hyslop W, Gerlach R, et al. Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy. Physica A, 2005, 356(1): 184-189.
19. Rosso O A, Martin M T, Figliola A, et al. EEG analysis using wavelet-based information tools. J Neurosci Methods, 2006, 153(2): 163-182.
20. Pereyra M E, Lamberti P W, Rosso O A. Wavelet Jensen-Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures. Physica A, 2007, 379(1): 122-132.
21. Acharya U R, Molinari F, Sree S, et al. Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control, 2012, 7(4): 401-408.
22. Wang Deng, Miao Duoqian, Xie Chen. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl, 2011, 38(11): 14314-14320.
23. Rosso O A, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods, 2001, 105(1): 65-75.

Journal of Biomedical Engineering

Wavelet entropy analysis for ictal electroencephalogram signals of child absence epilepsy

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