  Epilepsy is one of the most common brain diseases that affect more than 1\%
of the world's population. It is characterized by recurrent seizures, which
come in different types and are treated differently. Electroencephalography
(EEG) is commonly used in medical services to diagnose seizures and their
types. The accurate identification of seizures helps to provide optimal
treatment and accurate information to the patient. However, the manual
diagnostic procedures of epileptic seizures are laborious and
highly-specialized. Moreover, EEG manual evaluation is a process known to have
a low inter-rater agreement among experts. This paper presents a novel
automatic technique that involves extraction of specific features from EEG
signals using Dual-tree Complex Wavelet Transform (DTCWT) and classifying them.
We evaluated the proposed technique on TUH EEG Seizure Corpus (TUSZ) ver.1.5.2
dataset and compared the performance with existing state-of-the-art techniques
using overall F1-score due to class imbalance seizure types. Our proposed
technique achieved the best results of weighted F1-score of 99.1\% and 74.7\%
for seizure-wise and patient-wise classification respectively, thereby setting
new benchmark results for this dataset.
