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A channel-wise attention-based representation learning method for epileptic seizure detection and type classification
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  • Asma Baghdadi ,
  • Rahma fourati ,
  • yassine Aribi ,
  • Sawsan Daoud ,
  • Mariem Dammak ,
  • Chokri Mhiri ,
  • Patrick Siarry ,
  • Adel M. Alimi
Asma Baghdadi

Corresponding Author:[email protected]

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Rahma fourati
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yassine Aribi
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Sawsan Daoud
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Mariem Dammak
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Chokri Mhiri
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Patrick Siarry
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Adel M. Alimi
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Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a
crucial patient-dependent step for the treatment selection process. The selection of the proper treatment
relies on the correct identification of the seizure type. As such, identifying the seizure type has the
biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts
when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and
classification methods are conceptualized following the patient-dependent schema thus fail to perform
well with unknown cases. Our work focuses on patient-independent schema for seizure type classification
and pays more attention to the explainability of the underlying attention mechanism of our method.
Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled.
Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure
types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and
the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with
an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a
high correlation between the neurological baselines and the interpretable results of our model.
Jul 2023Published in Journal of Ambient Intelligence and Humanized Computing volume 14 issue 7 on pages 9403-9418. 10.1007/s12652-023-04609-6