Wavelet Analysis of Event Related Potentials for Early Diagnosis of Alzheimer’s Disease

  • Robi Polikar
  • Fritz Keinert
  • Mary Helen Greer
Chapter
Part of the Computational Imaging and Vision book series (CIVI, volume 19)

Abstract

Alzheimer’s disease, a neurological disorder claiming hundreds of thousands of lives every year, is the most common of all cortical dementias. Neurologists usually identify the disease from various symptoms; however, misdiagnosis is not uncommon. An autopsy is the only method for a definite diagnosis. Additional techniques to increase the accuracy of ante-mortem diagnoses are therefore necessary. In this study, evoked potentials of the electroencephalograms (EEGs) of a group of patients were analyzed, half of whom had been diagnosed with early Alzheimer’s disease. The EEGs were analyzed and processed using multiresolution wavelet analysis techniques, and processed signals were then used to train a neural network to distinguish the signals that belonged to patients with Alzheimer’s disease from those that belonged to patients without Alzheimer’s disease. We discuss why wavelet analysis is particularly well suited for these kind of signals, along with results demonstrating the feasibility of the approach.

Keywords

Discrete Wavelet Transform Wavelet Analysis Discrete Wavelet P300 Component Detail Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Robi Polikar
    • 1
  • Fritz Keinert
    • 2
  • Mary Helen Greer
    • 3
  1. 1.Dept. of Electrical and Comp. Eng.Rowan UniversityGlassboroUSA
  2. 2.Dept. of MathematicsIowa State UniversityAmesUSA
  3. 3.Dept. of Biomedical SciencesIowa State UniversityAmesUSA

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