Time-frequency complexity of EEG following hypoxic-ischemic brain injury

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, 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.