RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals.
Comput Biol Med
; 171: 108086, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38382383
ABSTRACT
Increasing attention is being given to machine learning methods designed to aid clinicians in treatment selection. Therefore, this has aroused a heightened focus on the auto-detect system of epilepsy utilizing electroencephalogram(EEG) data. However, in order for the recognition model to accurately capture a wide range of features related to channel, frequency, and temporal information, it is necessary to have EEG data that is correctly represented. To tackle this challenge, we propose a Residual-based Inception with Hybrid-Attention Network(RIHANet) to achieve automatic seizure detection. Initially, by employing Empirical Mode Decomposition and Short-time Fourier Transform(EMD-STFT) for data processing, it can improve the quality of time-frequency representation of EEG. Additionally, by applying a novel Residual-based Inception to the network architecture, the detection model can learn local and global multiscale spatial-temporal features. Furthermore, the Hybrid Attention model designed is used to obtain relationships between EEG signals from multiple perspectives, including channels, sub-spaces, and global. Using four public datasets, the suggested approach outperforms the results in the most recent scholarly publications.
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Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
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Epilepsia
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article