Multiresolution wavelet analysis of ERPs for the detection of Alzheimer's disease

Robi Polikar, Mary Helen Greer, Lalita Udpa, Fritz Keinert

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

Alzheimer's disease, a neurological disorder usually seen in elderly people, is the most common of all cortical dementias. It can only be diagnosed with certainty via an autopsy. Neurologists usually diagnose Alzheimer's disease from symptoms; however, misdiagnosis can be a problem. Additional techniques to increase the accuracy of ante-mortem diagnoses would be useful. In this study, event related potentials (ERPs) of two groups of patients were acquired. One group had been diagnosed as having Alzheimer's disease and the other group as not having Alzheimer's disease. The ERPs were analyzed using multiresolution wavelet analysis techniques. The analyzed signals were then used to train a multilayer perceptron (MLP) neural network to distinguish the signals belonging to the two groups of patients. Initial results demonstrated the feasibility of this approach.

Original languageEnglish (US)
Pages (from-to)1301-1304
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - Dec 1 1997
Externally publishedYes
EventProceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA
Duration: Oct 30 1997Nov 2 1997

All Science Journal Classification (ASJC) codes

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

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