Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease

Tyler Staudinger, Robi Polikar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neu-rofibrillary plaques and tangles in the brain that leads to neu-ronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects 79 with AD, and 82 age-matched controls (CN) are analyzed using several nonlinear signal complexity measures. These measures include: Hi-guchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60-66%, suggesting they contain some discriminatory information; however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages2033-2036
Number of pages4
DOIs
StatePublished - Dec 26 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint Dive into the research topics of 'Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease'. Together they form a unique fingerprint.

  • Cite this

    Staudinger, T., & Polikar, R. (2011). Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 2033-2036). [6090374] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/IEMBS.2011.6090374