Analysis of Electroencephalography Activity in Early Stage Alzheimer's Disease Using a Multiscale Statistical Complexity Measure

Gordon Morison*, Zoe Tieges, Kerry Kilborn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Alzheimer's Disease (AD) is a neurodegenerative disorder associated with progressive loss of cognitive function. This study examined the electroencephalography (EEG) of patients with mild AD and cognitively unimpaired older controls while they performed a memory task. A novel Multiscale extension of the Statistical Complexity Measure (SCM) was calculated by combining the Bandt and Pompe Permutation Entropy with the Wootters' distance disequilibrium methods. This algorithm was then applied to the EEG of patients and controls during task execution, and groups were compared in terms of complexity of the underlying brain signals at multiple temporal scales. In centro-temporal electrode regions, patients had significantly lowered EEG complexity in temporal scales 5 to 7 compared to controls (p < 0.001). No significant group differences in EEG complexity were found in smaller scales. Multiscale Permutation SCM (MPSCM) values correlated with cognitive behavioral measures (p < 0.01), indicating that the MPSCM results reflect, to some extent, task-related activity. The MPSCM may be a useful marker for deficits in task-specific processing of information in mild AD.
Original languageEnglish
Pages (from-to)2414-2418
JournalAdvanced Science Letters
Volume19
Issue number8
DOIs
Publication statusPublished - 1 Aug 2013

Keywords

  • Electroencephalography
  • Alzheimer's disease
  • Statistical Complexity Measure

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