LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection

Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To reduce the workload of doctors, some diagnostic auxiliary methods based on deep learning have been proposed. However, deep learning models often need more explainability, and even if they are explainable, their structure is usually complex. This paper presents a lightweight and explainable machine learning-based model named LightIED for detecting IEDs in EEG. The EEG data is first plotted as the image in the experiment and fed into the model for the IED detection task. Then, the Grad-CAM is used to analyze the output results and visualize the basis of inference. The detection accuracy of IEDs with the LightIED is almost equivalent to the current state-of-the-art (SoTA) model, Satelight, and higher than other Vision Transformer-based models. Moreover, the number of parameters is less than one-third compared to Satelight, the existing lightweight model. In addition, the visualizing results by Grad-CAM highlight the IEDs. Our results demonstrate that the proposed LightIED effectively detects IEDs with reasonable visualization.