Wavelet Entropy Analysis of Spontaneous EEG Signals in Alzheimer's Disease_Journal of Biomedical Engineering

Authors：

ZHANGMeiyun ^1,2 ,  ZHANGBenshu ² , CHENYing ²

1. Department of Neurology, Tianjin Union Medicine Centre, Tianjin 300121, China;
2. Department of Neurology, General Hospital, Tianjin Medical University, Tianjin 300052, China;

Corresponding author：

ZHANGBenshu, Email: Zhangbenshu2011@aliyun.com

Keywords：

Alzheimer's disease; electroencephalogram; wavelet analysis; wavelet entropy

DOI：

10.7507/1001-5515.20140141

Video：

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Abstract Full text Figures/Tables Video References Cited by

Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r=0.601-0.799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.

Citation： ZHANGMeiyun, ZHANGBenshu, CHENYing. Wavelet Entropy Analysis of Spontaneous EEG Signals in Alzheimer's Disease. Journal of Biomedical Engineering, 2014, 31(4): 755-761,770. doi: 10.7507/1001-5515.20140141 Copy

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2.	STAM C J, TAVY D L, KEUNEN R W. Quantification of alphy rhythm desynchronization using the acceleration spectrum entropy of the EEG[J]. Clin Electroencephalogr, 1993, 24(3): 104-109.
3.	MALLAT S G. A theory for multiresolution signal decomposition:the wavelet representation[J]. IEEE Tran on PAMI, 1989, 11(7): 674-693.
4.	BLANCO S, FIGLIOLA A, QUIAN QUIROGA R, et al. Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function[J]. Phys Rev E, 1998, 57(1): 932-940.
5.	WANG D, MIAO D, XIE C. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection[J]. Expert Syst Appl, 2011, 38(11): 14314-14320.
6.	郁洪强,赵欣,詹启生,等.基于子波熵的网络成瘾脑电复杂性分析[J].天津大学学报,2008,41(6):751-756.
7.	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[J]. Expert Syst Appl, 2009, 36(4): 7390-7399.
8.	KAR S, BHAGAT M, ROUTRAY A. EEG signal analysis for the assessment and quantification of driver's fatigue[J]. Trans Res Part F,2010, 13(5): 297-306.
9.	SLOBOUNOV S, CAO C, SEBASTIANELLI W. Differential effect of first versus second concussive episodes on wavelet information quality of EEG[J]. Clin Neurophys, 2009, 120(5): 862-867.
10.	ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals[J]. J Neurosci Methods, 2001, 105(1): 65-75.
11.	QUIROGA R Q, ROSSO O A, BA?AR E, et al. Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations[J]. Biol Cybern, 2001, 84(4): 291-299.
12.	BASAR E,SCHURMANN M, DEMIRALP T. Event-related oscillations are "real brain responses"--wavelet analysis and new strategies[J].Int J Psychophysiol, 2001, 39(2-3): 91-127.
13.	YORDANOVA J, KOLEV V, ROSSO O A, et al. Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance[J]. J Neurosci Methods, 2002, 117(1): 99-109.
14.	ROSSO O A. Entropy changes in brain function[J]. Int J Psychophysiol, 2007, 64(1): 75-80.
15.	ROSSO O A, MARTIN M T, FIGLIOLA A, et al. EEG analysis using wavelet-based information tools[J]. J Neurosci Methods, 2006, 153(2): 163-182.
16.	CEK M E, OZGOREN M, SAVACI F A. Continuous time wavelet entropy of auditory evoked potentials[J]. Comput Biol Med, 2010, 40(1): 90-96.
17.	WEISS S, RAPPELSBERGER P. Long-range EEG synchronization during word encoding correlates with successful memory performance[J]. Cognitive Brain Research, 2000, 9(3): 299-312.
18.	WEISS S, RAPPELSBERGER P. Left frotal EEG coherece reflects modality independent language processes[J]. Brain Topogr, 1998, 11(1): 33-42.
19.	KARRASCH M, LAINE M, RINNE J O, et al. Brain oscillatory responses to an auditory-verbal working memory task in mild congnitive impairment and Alzheimer's disease[J]. Int J Psychophysiol, 2006, 59(2): 168-178.

1. POWELL C E, PERCEIVAL I C. A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems[J]. Journal of Physics A: Mathematical and General, 1979, 12(11): 2053-2071.
2. STAM C J, TAVY D L, KEUNEN R W. Quantification of alphy rhythm desynchronization using the acceleration spectrum entropy of the EEG[J]. Clin Electroencephalogr, 1993, 24(3): 104-109.
3. MALLAT S G. A theory for multiresolution signal decomposition:the wavelet representation[J]. IEEE Tran on PAMI, 1989, 11(7): 674-693.
4. BLANCO S, FIGLIOLA A, QUIAN QUIROGA R, et al. Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function[J]. Phys Rev E, 1998, 57(1): 932-940.
5. WANG D, MIAO D, XIE C. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection[J]. Expert Syst Appl, 2011, 38(11): 14314-14320.
6. 郁洪强,赵欣,詹启生,等.基于子波熵的网络成瘾脑电复杂性分析[J].天津大学学报,2008,41(6):751-756.
7. 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[J]. Expert Syst Appl, 2009, 36(4): 7390-7399.
8. KAR S, BHAGAT M, ROUTRAY A. EEG signal analysis for the assessment and quantification of driver's fatigue[J]. Trans Res Part F,2010, 13(5): 297-306.
9. SLOBOUNOV S, CAO C, SEBASTIANELLI W. Differential effect of first versus second concussive episodes on wavelet information quality of EEG[J]. Clin Neurophys, 2009, 120(5): 862-867.
10. ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals[J]. J Neurosci Methods, 2001, 105(1): 65-75.
11. QUIROGA R Q, ROSSO O A, BA?AR E, et al. Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations[J]. Biol Cybern, 2001, 84(4): 291-299.
12. BASAR E,SCHURMANN M, DEMIRALP T. Event-related oscillations are "real brain responses"--wavelet analysis and new strategies[J].Int J Psychophysiol, 2001, 39(2-3): 91-127.
13. YORDANOVA J, KOLEV V, ROSSO O A, et al. Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance[J]. J Neurosci Methods, 2002, 117(1): 99-109.
14. ROSSO O A. Entropy changes in brain function[J]. Int J Psychophysiol, 2007, 64(1): 75-80.
15. ROSSO O A, MARTIN M T, FIGLIOLA A, et al. EEG analysis using wavelet-based information tools[J]. J Neurosci Methods, 2006, 153(2): 163-182.
16. CEK M E, OZGOREN M, SAVACI F A. Continuous time wavelet entropy of auditory evoked potentials[J]. Comput Biol Med, 2010, 40(1): 90-96.
17. WEISS S, RAPPELSBERGER P. Long-range EEG synchronization during word encoding correlates with successful memory performance[J]. Cognitive Brain Research, 2000, 9(3): 299-312.
18. WEISS S, RAPPELSBERGER P. Left frotal EEG coherece reflects modality independent language processes[J]. Brain Topogr, 1998, 11(1): 33-42.
19. KARRASCH M, LAINE M, RINNE J O, et al. Brain oscillatory responses to an auditory-verbal working memory task in mild congnitive impairment and Alzheimer's disease[J]. Int J Psychophysiol, 2006, 59(2): 168-178.

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