# Learning Robust Features using Deep Learning for Automatic Seizure Detection

arXiv: https://arxiv.org/abs/1608.00220 · HTML: https://mlsanity.com/paper/1608.00220/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2016.

## Built on by (most cited first, 16 of 16)

- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates](https://mlsanity.com/paper/2406.16906/) (2024)
- [VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG](https://mlsanity.com/paper/2311.14775/) (2023)
- [SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding](https://mlsanity.com/paper/2409.19884/) (2024)
- [Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network](https://mlsanity.com/paper/2105.10358/) (2021)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information](https://mlsanity.com/paper/2101.05608/) (2021)
- [EEG-based Image Feature Extraction for Visual Classification using Deep Learning](https://mlsanity.com/paper/2209.13090/) (2022)
- [Optimized preprocessing and Tiny ML for Attention State Classification](https://mlsanity.com/paper/2303.11371/) (2023)
- [SeizureFormer: A Transformer Model for IEA-Based Seizure Risk Forecasting](https://mlsanity.com/paper/2504.16098/) (2025)
- [EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network](https://mlsanity.com/paper/2505.15203/) (2025)
- [Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection](https://mlsanity.com/paper/2406.16908/) (2024)
- [Optimizing Speech Multi-View Feature Fusion through Conditional Computation](https://mlsanity.com/paper/2501.08057/) (2025)
- [MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection](https://mlsanity.com/paper/2503.08251/) (2025)
- [LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection](https://mlsanity.com/paper/2601.06016/) (2026)

## Abstract (arXiv)

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both inter- and intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invariant representation of a seizure. The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. Furthermore, our model proves to be robust to missing channel and variable electrode montage.