Abstract
Clinical assessment of neurological system would be greatly facilitated by interpretation of disease- or injury-related changes in electroencephalogram (EEG) signals. Autoregressive (AR) models have previously been used to parameterize the EEG signals. Cepstral distance which is based on the AR model parameters, is used here to measure the similarity of the EEG signals obtained during various phases of the experimental study. The effectiveness of the cepstral distance is demonstrated through its ability to distinguish asphyxia and quantify the period of the brain asphyxia injury.
Original language | English (US) |
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Pages (from-to) | 1205-1206 |
Number of pages | 2 |
Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
Volume | 3 |
State | Published - Dec 1 1997 |
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 |
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics