A hybrid CNN transformer framework integrated with time frequency representation for epileptic seizure detection...

Epileptic seizures represent the most widespread neurological disorder in the world. The efficient automatic detection of seizures is crucial for timely diagnosis and prompt treatment. In this paper, we propose a novel framework, IIRB-HybridFormer, that integrates a hybrid CNN transformer framework with time frequency representation for epileptic seizure detection from Electroencephalogram (EEG) signals. The proposed Inception-based Inverted Residual Bottleneck (IIRB) block utilizes a multi-branch feature extraction mechanism in conjunction with an inverted residual structure, ensuring efficient feature representation with minimal computational overhead. In the subsequent stage, Long Short-Term Memory (LSTM) is integrated with the Hybrid Transformer (HybridFormer) to eliminate the need for positional encoding, thereby reducing the computational complexity. The LSTM-derived feature vector is embedded in HybridFormer, which leverages Hybrid Pooled Multi-head Attention (HPMA) and an Inverted Residual Bottleneck-based Feed-forward Network (IRBFN) to enable robust local and global feature extraction. The algorithm's effectiveness is evaluated on two publicly available EEG databases of varying sizes, namely the Bonn and CHB-MIT datasets. Experimental results demonstrate classification accuracy of 99.538% and 97.801% on the Bonn and CHB-MIT, respectively. The CHB-MIT dataset is also evaluated for event-based seizure detection, achieving a sensitivity of 100% and a false detection rate of 2.474/h. The proposed architecture exhibits a superior performance compared to the existing state-of-the-art models. It also surpasses other pre-trained models with its lightweight framework, along with reduced training and testing time. The results validate that the proposed IIRB-HybridFormer framework is robust and reliable in highlighting its strong potential for effective detection of epileptic seizures.