RIHANet: A Residual-based Inception with Hybrid-Attention Network for Seizure Detection using EEG signals

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.