# Convolutional neural network for detection and classification of seizures in clinical data

arXiv: https://arxiv.org/abs/1903.08864 · HTML: https://mlsanity.com/paper/1903.08864/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates](https://mlsanity.com/paper/2406.16906/) (2024)
- [A Meta-GNN approach to personalized seizure detection and classification](https://mlsanity.com/paper/2211.02642/) (2022)
- [Towards trustworthy seizure onset detection using workflow notes](https://mlsanity.com/paper/2306.08728/) (2023)
- [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)
- [Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection](https://mlsanity.com/paper/2304.06038/) (2023)
- [From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks](https://mlsanity.com/paper/2606.22258/) (2026)

## Abstract (arXiv)

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data.