  Epilepsy is one of the most prevalent neurological conditions, where an
epileptic seizure is a transient occurrence due to abnormal, excessive and
synchronous activity in the brain. Electroencephalogram signals emanating from
the brain may be captured, analysed and then play a significant role in
detection and prediction of epileptic seizures. In this work we enhance upon a
previous approach that relied on the differing properties of the wavelet
transform. Here we apply the Maximum Overlap Discrete Wavelet Transform to both
reduce signal \textit{noise} and use signal variance exhibited at differing
inherent frequency levels to develop various metrics of connection between the
electrodes placed upon the scalp. %The properties of both the noise reduced
signal and the interconnected electrodes differ significantly during the
different brain states.
  Using short duration epochs, to approximate close to real time monitoring,
together with simple statistical parameters derived from the reconstructed
noise reduced signals we initiate seizure detection. To further improve
performance we utilise graph theoretic indicators from derived electrode
connectivity. From there we build the attribute space. We utilise open-source
software and publicly available data to highlight the superior
Recall/Sensitivity performance of our approach, when compared to existing
published methods.
