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Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease

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Abstract

The complexity change of brain activity in Alzheimer’s disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.

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Acknowledgements

This work is supported by Tianjin Municipal Natural Science Foundation under Grants 13JCZDJC27900, Tangshan Science and Technology Support Project under Grants 14130223B and Jilin Provincial Natural Science Foundation under Grants 20130101170JC.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Tianjin University, Tianjin, China

    Bin Deng, Lihui Cai, Shunan Li, Haitao Yu & Jiang Wang

  2. School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China

    Ruofan Wang

  3. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China

    Yingyuan Chen

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  1. Bin Deng
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  2. Lihui Cai
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  3. Shunan Li
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  4. Ruofan Wang
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  5. Haitao Yu
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  6. Yingyuan Chen
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  7. Jiang Wang
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Correspondence to Jiang Wang.

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Deng, B., Cai, L., Li, S. et al. Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease. Cogn Neurodyn 11, 217–231 (2017). https://doi.org/10.1007/s11571-016-9418-9

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  • DOI: https://doi.org/10.1007/s11571-016-9418-9

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