  Epileptic seizure detection and classification in clinical
electroencephalogram data still is a challenge, and only low sensitivity with a
high rate of false positives has been achieved with commercially available
seizure detection tools, which usually are patient non-specific. Epilepsy
patients suffer from severe detrimental effects like physical injury or
depression due to unpredictable seizures. However, even in hospitals due to the
high rate of false positives, the seizure alert systems are of poor help for
patients as tools of seizure detection are mostly trained on unrealistically
clean data, containing little noise and obtained under controlled laboratory
conditions, where patient groups are homogeneous, e.g. in terms of age or type
of seizures. In this study authors present the approach for detection and
classification of a seizure using clinical data of electroencephalograms and a
convolutional neural network trained on features of brain synchronisation and
power spectrum. Various deep learning methods were applied, and the network was
trained on a very heterogeneous clinical electroencephalogram dataset. In
total, eight different types of seizures were considered, and the patients were
of various ages, health conditions and they were observed under clinical
conditions. Despite this, the classifier presented in this paper achieved
sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a
significant improvement as compared to the known results for clinical data.
