Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance

Neonatal seizure detection is a complex task which requires careful interpretation of the neonatal electroencephalogram (EEG) waveforms. Often seizures in newborn babies are the sole indicator of a serious neurological condition [1]. Unlike seizures in children and adults, most neonatal seizures present no physical signs and the reliable detection of seizures is only possible through EEG analysis [2]. Specially trained staff and equipment are required to detect seizures, limiting diagnosis and treatment to specialized units. These challenges have prompted research into the development of computer-based automated seizure detection algorithms. Automated seizure detection algorithms can provide objective support by alerting clinicians and aiding the early detection and treatment of seizures [3]. When detecting seizures an expert understands that background EEG is random in nature, whereas seizures represent a more ordered, rhythmic and evolving deviation from this background behaviour. This clinical knowledge has prompted the search for features which characterise the repetitiveness, order and predictability of EEG, and the application of machine learning classifiers to these features [4].