Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel–Ziv complexity

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• 作者：Xiaokun Liu ; Chunlai Zhang ; Zheng Ji ; Yi Ma ; Xiaoming Shang…
• 关键词：Electroencephalogram ; Alzheimer’s disease ; Power spectrum density ; Lempel–Ziv complexity ; Multi ; scale Lempel–Ziv complexity
• 刊名：Cognitive Neurodynamics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：10
• 期：2
• 页码：121-133
• 全文大小：5,802 KB
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• 作者单位：Xiaokun Liu (1)
  Chunlai Zhang (1)
  Zheng Ji (1)
  Yi Ma (1)
  Xiaoming Shang (1)
  Qi Zhang (1)
  Wencheng Zheng (1)
  Xia Li (1)
  Jun Gao (1)
  Ruofan Wang (2)
  Jiang Wang (2)
  Haitao Yu (2)
  
  1. Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000, Hebei, People’s Republic of China
  2. School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People’s Republic of China
  
• 刊物主题：Biomedicine general; Neurosciences; Computer Science, general; Artificial Intelligence (incl. Robotics); Biochemistry, general; Cognitive Psychology;
• 出版者：Springer Netherlands
• ISSN：1871-4099

文摘

To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer’s disease (AD), power spectrum density (PSD) and Lempel–Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel–Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.