Detection of neonatal seizure using multiple filters

It is often impossible to accurately differentiate between seizure and non-seizure related activities in infants based on clinical manifestations alone. The electroencephalogram (EEG) is therefore the best tool available for the recognition, management, and prognosis of neonatal seizures. The EEG signal is known to change structural characteristics between seizure and non-seizure states. In this work, matching pursuit (MP) decomposition, based on a coherent time-frequency (TF) dictionary, has provided us with a measure for quantifying changes in the structure of the neonatal EEG signal as it alternates between the various states. The quantification of state changes served as the basis for detecting seizures in 35 newborn patients. For each record, a patient-dependent threshold that marks the transition to seizure state is established. The use of multiple filters reduced the amount of artifacts and enhanced the detector performance. Overall, 93.4% detection accuracy and 0.26 false alarms per hour were achieved.