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Investigation of Alzheimer’s Disease EEG Frequency Components with Lempel-Ziv Complexity

  • Samantha Simons
  • Daniel Abasolo
  • Michael Hughes
Part of the IFMBE Proceedings book series (IFMBE, volume 45)

Abstract

This pilot study applied Lempel-Ziv Complexity (LZC) to 22 resting EEG signals, collected using the 10-20 international system, from 11 patients with Alzheimer’s disease (AD) and 11 age-matched controls. This allowed for frequency band analysis as the EEG signals were first pre-filtered with a third order Hamming window in the ranges F to F+WHz with both F and W equal to 1-30Hz respectively. Control subjects were found to have a greater signal complexity than AD patients with statistically significant bands seen at various ranges in all 16 electrodes. The maximum statistical significance (Student’s t test, p<0.01) was increased over the findings with traditional signal filtering techniques allowing the whole range, with a maximum significance of p=3.50e− 6 at electrode T4 between 7-18Hz. Electrode F4 also showed significantly high statistically significant differences. The maximum accuracy, both controls and AD patients correctly identified, found with Receiver Operating Characteristic Curves was 95.45% (21 of 22 subjects correctly classified) at T4 (7-18Hz and 7-20Hz), Fp2 (8-32Hz) and F4 (6-21Hz), which is significantly more accurate than the most accurate methods previously applied to this dataset. The beta band (13-30Hz) was found to be most influential in separating the two test groups in this study with the best range suggested to be 5-26Hz, combining traditional theta, alpha and beta bands. These findings suggest pre-filtering has a significant effect on method outcomes and can be successfully tailored to improve the statistical effectiveness of LZC at distinguishing between these two EEG groups. However, more testing is required to investigate the effectiveness at distinguishing other signal dynamics.

Keywords

Alzheimer’s disease Electroencephalogram Non-linear analysis Lempel-ziv complexity Frequency component analysis 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Samantha Simons
    • 1
  • Daniel Abasolo
    • 1
  • Michael Hughes
    • 1
  1. 1.Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordUK

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