Abstract
The complexity of EEG signal has been extensively studied in different domains such as time, frequency and chaotic index. In this study we define a novel measure, time frequency complexity (TFC), based on the matching pursuit (MP) algorithm. It describes the structural complexity of EEG signals from the joint time-frequency distribution of the signals. The MP algorithm, introduced by Mallat and Zhang [1], describes a general procedure to compute adaptive signal representations by decomposing a signal into a linear expansion with redundant basis functions, called atoms. We define the TFC of EEG with the Shannon entropy in the time-frequency plane computed by the MP algorithm. TFC is shown to be sensitive to the structural change (such as spiky/bursting activity) in the EEG signal following brain injury and its recovery. We studied the EEG of 5 min of hypoxic-ischemic (HI) brain injury. The preliminary results show that TFC could be useful for indicating different stages of brain injury and the recovery.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2570-2573 |
| Number of pages | 4 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 3 |
| State | Published - 2003 |
| Event | A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico Duration: Sep 17 2003 → Sep 21 2003 |
Keywords
- Brain injury
- EEG
- Entropy
- Matching pursuits
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics