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Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information

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Abstract

This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.

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Acknowledgment

This research was funded by National Natural Science Foundation of China (61876165, 61503326), Natural Science Foundation of Hebei Province in China (F2016203343), China Postdoctoral Science Foundation (2015M581317). The authors have no any potential conflicts.

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  1. Yijun Liu, Xiaodong Xu and Yanhong Zhou contributed equally to this paper.

Authors and Affiliations

  1. School of Science, Yanshan University, Qinhuangdao, China

    Yijun Liu

  2. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China

    Yijun Liu, Xiaodong Xu & Jian Xu

  3. School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China

    Yanhong Zhou

  4. Department of Biomedical Engineering, Chengde Medical University, Chengde, China

    Xianling Dong

  5. The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Xiaoli Li

  6. Department of Neurology, The Rocket Force Hospital of Chinese People’s Liberation Army, Beijing, China

    Shimin Yin

  7. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China

    Dong Wen

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  1. Yijun Liu
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Liu, Y., Xu, X., Zhou, Y. et al. Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information. Cogn Neurodyn 15, 987–997 (2021). https://doi.org/10.1007/s11571-021-09682-1

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