Automatic classification of epliptic seizure types in EEG datacould enable more precise diagnosis and efficient manage-ment of the disease. Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts , high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical data. 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  .
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts , high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for accurate cross-patient seizure type classification . Experiments on the recently released TUH EEG Seizure Corpus show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset . We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the classification accuracy of smaller networks through knowledge distillation for applications with low-memory and fast inference speed requirements .
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epliptic
epileptic
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EEG datacould
electroencephalograms (EEGs) data can
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manage-ment
management
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Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue
This task is challenging due
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signal to noise
signal-to-noise
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arte-facts
artefacts
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the
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individ-ual
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adeep learning based
SeizureNet, a deep learning
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multi-spectralfeature embeddings using multiple CNN models in an ensem-ble
multi-spectral feature embeddings using an ensemble
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clas-sification
classification
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EEGSeizure
EEG Seizure
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our multi-spectral dense featurelearning produces a weighted f1 score
SeizureNet produces state-of-the-art weighted F1 scores
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typeclassification
type classification
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. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the classification accuracy of smaller networks through knowledge distillation for applications with low-memory and fast inference speed requirements