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Epilepsy is one of the most prevalent neurological conditions, where anepileptic seizure is a transient occurrence due to abnormal, excessive andsynchronous activity in the brain. Electroencephalogram signals emanating fromthe brain may be captured, analysed and then play a significant role indetection and prediction of epileptic seizures. In this work we enhance upon aprevious approach that relied on the differing properties of the wavelettransform. Here we apply the Maximum Overlap Discrete Wavelet Transform to bothreduce signal \textit{noise} and use signal variance exhibited at differinginherent frequency levels to develop various metrics of connection between theelectrodes placed upon the scalp. %The properties of both the noise reducedsignal and the interconnected electrodes differ significantly during thedifferent brain states.  Using short duration epochs, to approximate close to real time monitoring,together with simple statistical parameters derived from the reconstructednoise reduced signals we initiate seizure detection. To further improveperformance we utilise graph theoretic indicators from derived electrodeconnectivity. From there we build the attribute space. We utilise open-sourcesoftware and publicly available data to highlight the superiorRecall/Sensitivity performance of our approach, when compared to existingpublished methods.

Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics