Automatic epileptic seizure analysis is important because the differentiation of neural patterns among different patients can be used to classify people with specific types of epilepsy. This could enable more efficient management of the disease. 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 .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.
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.
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epileptic seizure analysis is important because the differentiation of neural patterns among different patients can be used to classify people with specific types of epilepsy. This could enable more efficient management
classification of epliptic seizure types in EEG datacould enable more precise diagnosis and efficient manage-ment
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classification using
classificationusing
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challenging due
challengingdue
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artefacts
arte-facts
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individual
individ-ual
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clinical data constraints
availability of clinical data
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a deep
adeep
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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
learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture
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classification. We evaluate our framework for seizure type classification and seizure detection
clas-sification. Experiments
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EEG Seizure Corpus , where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby
EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification