Automatic seizure detection in the newborn: methods and initial evaluation.

Abstract

Seizures are most common in the newborn period, but at that age seizures can be very difficult to identify by clinical observation. Therefore the EEG plays an even greater role in newborns than in older children and adults. The electrographic features of seizures and EEG background in the newborn are, however, very different to those found in adults. We present a set of methods for the automatic detection of seizures in the newborn. The methods are aimed at detecting a wide range of patterns, including rhythmic paroxysmal discharges at a wide range of frequencies, as well as repetitive spike patterns, even when they are not very rhythmic. The methods were developed using EEGs obtained from 55 newborns, recorded at 3 hospitals that used differing monitoring protocols. A total of 281 h of recordings containing 679 seizures were analyzed. An initial evaluation indicated that 71% of the seizures and 78% of seizure clusters (group of seizures separated by less than 90 s) were detected, with a false detection rate of 1.7/h. The methods were developed so that they can be implemented to operate in real time.