Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection

• URL: http://arxiv.org/abs/2509.05190v1
• Date: Fri, 05 Sep 2025 15:42:15 GMT
• Title: Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection
• Authors: Mounvik K, N Harshit,
• Abstract summary: We present a lightweight one-dimensional CNN model with structured pruning to improve efficiency and reliability.<n>The model was trained with mild early stopping to address possible overfitting, achieving an accuracy of 92.78% and a macro-F1 score of 0.8686.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models, especially convolutional neural networks (CNNs), have shown considerable promise for biomedical signals such as EEG-based seizure detection. However, these models come with challenges, primarily due to their size and compute requirements in environments where real-time detection or limited resources are available. In this study, we present a lightweight one-dimensional CNN model with structured pruning to improve efficiency and reliability. The model was trained with mild early stopping to address possible overfitting, achieving an accuracy of 92.78% and a macro-F1 score of 0.8686. Structured pruning of the baseline CNN involved removing 50% of the convolutional kernels based on their importance to model predictions. Surprisingly, after pruning the weights and memory by 50%, the new network was still able to maintain predictive capabilities, while modestly increasing precision to 92.87% and improving the macro-F1 score to 0.8707. Overall, we present a convincing case that structured pruning removes redundancy, improves generalization, and, in combination with mild early stopping, achieves a promising way forward to improve seizure detection efficiency and reliability, which is clear motivation for resource-limited settings.

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