EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer
This work addresses epilepsy detection for medical diagnosis, presenting an incremental hybrid method.
The paper tackled epilepsy detection from EEG signals by proposing EENED, an end-to-end model combining CNN and Transformer to capture both local and global features, aiming to improve accuracy and reliability.
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing. Transformer models can capture the global dependencies in EEG signals through a self-attention mechanism, while CNN models can capture local features such as sawtooth waves. In this work, we propose an end-to-end neural epilepsy detection model, EENED, that combines CNN and Transformer. Specifically, by introducing the convolution module into the Transformer encoder, EENED can learn the time-dependent relationship of the patient's EEG signal features and notice local EEG abnormal mutations closely related to epilepsy, such as the appearance of spikes and the sprinkling of sharp and slow waves. Our proposed framework combines the ability of Transformer and CNN to capture different scale features of EEG signals and holds promise for improving the accuracy and reliability of epilepsy detection. Our source code will be released soon on GitHub.