# An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

arXiv: https://arxiv.org/abs/1801.05412 · HTML: https://mlsanity.com/paper/1801.05412/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

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

- [Self-supervised Contrastive Learning for EEG-based Sleep Staging](https://mlsanity.com/paper/2109.07839/) (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)
- [Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks](https://mlsanity.com/paper/2206.09951/) (2022)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data](https://mlsanity.com/paper/2102.01647/) (2021)
- [Automated Human Mind Reading Using EEG Signals for Seizure Detection](https://mlsanity.com/paper/2111.03270/) (2021)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks](https://mlsanity.com/paper/2104.07809/) (2021)
- [A Memristor-Inspired Computation for Epileptiform Signals in Spheroids](https://mlsanity.com/paper/2307.04607/) (2023)
- [MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings](https://mlsanity.com/paper/2505.17972/) (2025)
- [NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detection](https://mlsanity.com/paper/2508.08124/) (2025)
- [Accuracy-Constrained CNN Pruning for Efficient and Reliable EEG-Based Seizure Detection](https://mlsanity.com/paper/2509.05190/) (2025)
- [Deep Learning Classification of EEG Responses to Multi-Dimensional Transcranial Electrical Stimulation](https://mlsanity.com/paper/2512.20319/) (2025)
- [Combining Residual U-Net and Data Augmentation for Dense Temporal Segmentation of Spike Wave Discharges in Single-Channel EEG](https://mlsanity.com/paper/2601.00459/) (2026)
- [Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets](https://mlsanity.com/paper/2605.03874/) (2026)
- [Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective](https://mlsanity.com/paper/2609.34652/) (2026)

## Abstract (arXiv)

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.