Before revision: To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type classification. We evaluate our framework for seizure type classification and seizure detection on the recently released TUH EEG Seizure Corpus , where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby setting new benchmarks on the dataset.
After revision: To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type clas-sification. Experiments on the recently released TUH EEG Seizure Corpus , where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby setting new benchmarks on the dataset.