Before revision:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
After revision:  To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset .