Automated neonatal spike train detection as part of a neonatal seizure detection system

Neonatal seizures are an important sign of central nervous system dysfunction and require immediate medical attention. In this paper a new algorithm is presented for the detection of seizures in the electroencephalogram (EEG) of neonates. In contrast to the common approach in the literature, we define two (rather than one) types of seizures. This paper presents a new algorithm for the detection of one of these seizure types, namely a spike train, in the electroencephalogram (EEG) of neonates. The sensitivity of this algorithm is 98%, the positive predictive value 86% with a false positive rate of 0.6 per hour. Preliminary results on this subset indicate a clinically usable algorithm and outperform other published methods. An algorithm for the second seizure type was also developed but will be explained in a follow-up paper.