Automatic Classification and Explanation of Abnormal EEGs Using Signal Images and Spectrograms

Electroencephalogram (EEG) analysis is important for diagnosing neurological conditions but is often time-consuming and requires expert interpretation. This study focuses on automating the classification of normal and abnormal EEGs using the Temple University Hospital Abnormal EEG Dataset. We employed a ResNet50-based transfer learning method, utilizing 19-channel spectrograms and signal images as input to develop the method. Unlike previous methods that only analyzed specific segments of EEG data, we used entire EEGs to ensure comprehensive coverage, and our method achieved robust performance. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to provide explanations by highlighting the regions influencing method decisions. This method offers a promising step toward more efficient and interpretable automated EEG analysis.