A theta-band EEG based index for early diagnosis of Alzheimer's disease

Esteve Gallego-Jutglà, Jordi Solé-Casals, François Benoît Vialatte, Justin Dauwels, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

Despite recent advances, early diagnosis of Alzheimer's disease (AD) from electroencephalography (EEG) remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the band. This particular increase of synchrony is used with the well-known decrease of synchrony in the α band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients and healthy subjects is increased using the proposed ratio.

Original languageEnglish
Pages (from-to)1175-1184
Number of pages10
JournalJournal of Alzheimer's Disease
Volume43
Issue number4
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Alzheimer's disease
  • data interpretation
  • electroencephalography
  • mild cognitive impairment
  • phase synchronization

Fingerprint

Dive into the research topics of 'A theta-band EEG based index for early diagnosis of Alzheimer's disease'. Together they form a unique fingerprint.

Cite this