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 language | English (US) |
|---|---|
| Pages (from-to) | 1301-1304 |
| Number of pages | 4 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 3 |
| State | Published - 1997 |
| Externally published | Yes |
| Event | Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA Duration: Oct 30 1997 → Nov 2 1997 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics