A Lightweight CNN-LSTM Approach for Accurate EEG Normal-Abnormal Classification

Electroencephalogram (EEG) is an important tool in diagnosing neurological conditions, with normal abnormal classification often serving as the first step in identifying abnormalities associated with various diseases. However, many previous methods for EEG classification have relied on nonpublic datasets, limiting accessibility and reproducibility. In this study, we propose a lightweight CNN-LSTM-based model for classifying normal and abnormal EEGs using the publicly available TUH abnormal EEG dataset. Unlike previous methods that require multiple EEG channels, our approach uses only the T5-O1 channel, reducing complexity and computational costs. Additionally, our model utilizes convolutional layers for automatic feature selection, allowing it to identify important features without manual intervention, improving efficiency. We also apply Gradient-weighted Class Activation Mapping to visualize the model's output, enhancing interpretability.