Article ; Online: RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals.
Computers in biology and medicine
2024 Volume 171, Page(s) 108086
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 ... ...
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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MeSH term(s) | Humans ; Signal Processing, Computer-Assisted ; Seizures/diagnosis ; Epilepsy/diagnosis ; Machine Learning ; Electroencephalography/methods ; Algorithms |
Language | English |
Publishing date | 2024-02-05 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 127557-4 |
ISSN | 1879-0534 ; 0010-4825 |
ISSN (online) | 1879-0534 |
ISSN | 0010-4825 |
DOI | 10.1016/j.compbiomed.2024.108086 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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