Before revision:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints .
After revision:  Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts , high variance in the seizure semiology among individ-ual epileptic patients, and limited clinical data constraints .