  Neonatal seizures are a commonly encountered neurological condition. They are
the first clinical signs of a serious neurological disorder. Thus, rapid
recognition and treatment are necessary to prevent serious fatalities. The use
of electroencephalography (EEG) in the field of neurology allows precise
diagnosis of several medical conditions. However, interpreting EEG signals
needs the attention of highly specialized staff since the infant brain is
developmentally immature during the neonatal period. Detecting seizures on time
could potentially prevent the negative effects on the neurocognitive
development of the infants. In recent years, neonatal seizure detection using
machine learning algorithms have been gaining traction. Since there is a need
for the classification of bio-signals to be computationally inexpensive in the
case of seizure detection, this research presents a machine learning (ML) based
architecture that operates with comparable predictive performance as previous
models but with minimum level configuration. The proposed classifier was
trained and tested on a public dataset of NICU seizures recorded at the
Helsinki University Hospital. Our architecture achieved a best sensitivity of
87%, which is 6% more than that of the standard ML model chosen in this study.
The model size of the ML classifier is optimized to just 4.84 KB with minimum
prediction time of 182.61 milliseconds, thus enabling it to be deployed on
wearable ultra-edge devices for quick and accurate response and obviating the
need for cloud-based and other such exhaustive computational methods.
