  Epilepsy is one of the most common and yet diverse set of chronic
neurological disorders. This excessive or synchronous neuronal activity is
termed seizure. Electroencephalogram signal processing plays a significant role
in detection and prediction of epileptic seizures. In this paper we introduce
an approach that relies upon the properties of wavelets for seizure detection.
We utilise the Maximum Overlap Discrete Wavelet Transform which enables us to
reduce signal noise Then from the variance exhibited in wavelet coefficients we
develop connectivity and communication efficiency between the electrodes as
these properties differ significantly during a seizure period in comparison to
a non-seizure period. We use basic statistical parameters derived from the
reconstructed noise reduced signal, electrode connectivity and the efficiency
of information transfer to build the attribute space.
  We have utilised data that are publicly available to test our method that is
found to be significantly better than some existing approaches.
