Detecting Neonatal Seizures using Sample Covariance Estimation
Aleksandar M. Jeremic, Dejan Nikolić · 2019
One of the most frequent of neurological dysfunctions in prematurely born infants is the presence of frequent seizures. As they may be related to serious neurological problems they require immediate detection which is most commonly done using electroencephalography (EEG) systems that enable trained physicians to detect them in the real time. Due to the length of neonatal period (first 28 days) it would be extremely beneficial to have an automated system that is able to detect seizures as it would enable more efficient use of expert time. In this paper we propose a new multichannel technique for detecting seizure in neonates that calculates distance measure using second order statistical properties and Frechet mean. We have demonstrated previously that Frechet mean in certain cases can outperform clustering/detection algorithms that are based on first order distances.