Epileptic Seizure Detection Based on Path Signature and Bi-LSTM Network With Attention Mechanism.
IEEE Trans Neural Syst Rehabil Eng
; 32: 304-313, 2024.
Article
in En
| MEDLINE
| ID: mdl-38224524
ABSTRACT
Automatic seizure detection using electroen-cephalogram (EEG) can significantly expedite the diagnosis of epilepsy, thereby facilitating prompt treatment and reducing the risk of future seizures and associated complications. While most existing EEG-based epilepsy detection studies employ deep learning models, they often ignore the chronological relationships between different EEG channels. To tackle this limitation, a novel automatic epilepsy detection method is proposed, which leverages path signature and Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with an attention mechanism. The path signature algorithm is used to extract discriminative features for capturing the dynamic dependencies between different channels of EEG, while Bi-LSTM with attention further analyzes the inherent temporal dependencies hidden in EEG signal features. Our method is evaluated on two public EEG databases with different sizes (CHB-MIT and TUEP) and a private database from a local hospital. Two experimental settings are used, i.e., five-fold cross-validation and leave-one-out cross-validation. Experimental results show that our method achieves 99.09%, 95.60%, and 99.87% average accuracies on CHB-MIT, TUEP with 250Hz, and TUEP with 256Hz, respectively. On the private dataset, our method also achieves 99.40% average accuracy, which outperforms other methods. Furthermore, our method exhibits robustness in patients, as demonstrated by the evaluation results of cross-patient experiments.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electroencephalography
/
Epilepsy
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
IEEE Trans Neural Syst Rehabil Eng
Journal subject:
ENGENHARIA BIOMEDICA
/
REABILITACAO
Year:
2024
Document type:
Article