Time-Frequency Complexity of EEG Following Hypoxic-Ishemic Brain Injury

S. Tong, N. V. Thakor

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


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 languageEnglish (US)
Pages (from-to)2570-2573
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 2003
EventA 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 2003Sep 21 2003


  • Brain injury
  • EEG
  • Entropy
  • Matching pursuits

ASJC Scopus subject areas

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


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