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

arXiv: https://arxiv.org/abs/2509.05190 · HTML: https://mlsanity.com/paper/2509.05190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach](https://mlsanity.com/paper/1801.05412/) (2018)
- [Adaptive Gradient Methods with Local Guarantees](https://mlsanity.com/paper/2203.01400/) (2022)
- [Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging](https://mlsanity.com/paper/2402.16221/) (2024)
- [Parkinson's Disease Classification via EEG: All You Need is a Single Convolutional Layer](https://mlsanity.com/paper/2408.10457/) (2024)

## Abstract (arXiv)

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.