  Identifying epileptic seizures through analysis of the electroencephalography
(EEG) signal becomes a standard method for the diagnosis of epilepsy. Manual
seizure identification on EEG by trained neurologists is time-consuming,
labor-intensive and error-prone, and a reliable automatic seizure/non-seizure
classification method is needed. One of the challenges in automatic
seizure/non-seizure classification is that seizure morphologies exhibit
considerable variabilities. In order to capture essential seizure patterns,
this paper leverages an attention mechanism and a bidirectional long short-term
memory (BiLSTM) to exploit both spatial and temporal discriminating features
and overcome seizure variabilities. The attention mechanism is to capture
spatial features according to the contributions of different brain regions to
seizures. The BiLSTM is to extract discriminating temporal features in the
forward and the backward directions. Cross-validation experiments and
cross-patient experiments over the noisy data of CHB-MIT are performed to
evaluate our proposed approach. The obtained average sensitivity of 87.00%,
specificity of 88.60% and precision of 88.63% in cross-validation experiments
are higher than using the current state-of-the-art methods, and the standard
deviations of our approach are lower. The evaluation results of cross-patient
experiments indicate that, our approach has better performance compared with
the current state-of-the-art methods and is more robust across patients.
