Newborn EEG Seizure Detection Using Time-Frequency Matched Filtering

Abstract Background: The analysis of Electroencephalography (EEG) signals acquired from epileptic babies shows that seizures can be modeled as piecewise linear frequency modulated (LFM) signals. This fact motivated the use of time-frequency matched filters (TFMFs) for seizure detection in newborn EEG. A TFMF is characterized by a unique test statistic, which is found based on the time-frequency (TF) correlation between the signal under analysis and a template. The test statistic is compared with a threshold to determine the presence or absence of the template in the signal under analysis. Objectives: We present two seizure detection algorithms based on the general class of TFMFs and an improved algorithm in the ambiguity domain and evaluate their performance using real EEG signals. Methods: The method includes the following stages: Based on TF analysis of newborn EEG, we create a template set containing M piecewise LFM signals with L pieces and slopes. We define test statistics based on the TF correlation...