Detecting epileptic seizure through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. In a manual way, monitoring of long term EEG is tedious and error prone. Therefore, a reliable automatic seizure detection method is desirable. A critical challenge to automatic seizuredetection 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) model to exploit both spatially and temporally discriminating features and account for seizure variabilities. The attention mechanism is to capture spatial features more effectively according to the contributions of brain areas to seizures. The BiLSTM model is to extract more discriminating temporal features in the forward and the backward directions. By accounting for both spatial and temporal variations of seizures, the proposed method is more robust across subjects. The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods. In both mixing-patients and cross-patient experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison .
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 .
R
Detecting epileptic seizure
Identifying epileptic seizures
R
In a manual way, monitoring of long term EEG is tedious and error prone. Therefore,
Manual seizure identification on EEG by trained neurologists is time-consuming, labor-intensive and error-prone, and
R
detection method is desirable. A critical challenge to automatic seizuredetection
/non-seizure classification method is needed. One of the challenges in automatic seizure/non-seizure classification
D
model
R
spatially and temporally
spatial and temporal
R
account for
overcome
D
more effectively
R
brain areas
different brain regions
D
model
D
more
R
By accounting for both spatial and temporal variations of seizures, the proposed method is more robust across subjects. The testing results
Cross-validation experiments and cross-patient experiments
D
real
R
show that the proposed method outperforms the current
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
R
methods. In both mixing-patients and
methods, and the standard deviations of our approach are lower. The evaluation results of
R
experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison
experiments indicate that, our approach has better performance compared with the current state-of-the-art methods and is more robust across patients