Gaussian process modelling as an indicator of neonatal seizure
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
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Previous techniques for seizure detection in newborn babies are inefficient. The main reason for their relative poor performance resides in their assumption of stationarity of the EEG. To remedy this problem, we use time-frequency distributions (TFD) to analyse and characterise the newborn EEG seizure patterns as a first step toward a time-frequency (TF) based seizure detection and classification scheme. This paper presents the results of the analysis of these time-frequency patterns for two abnormal newborn EEGs. We demonstrate that the newborn EEG seizures are well described by a class of mono- and multi-component linear FM signals. This result is novel and contradicts the simplistic assumptions routinely made in the field.