# Neural Network Based Epileptic EEG Detection and Classification

arXiv: https://arxiv.org/abs/2111.03268 · HTML: https://mlsanity.com/paper/2111.03268/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis](https://mlsanity.com/paper/2003.04534/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions](https://mlsanity.com/paper/2407.04302/) (2024)

## Abstract (arXiv)

Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatments are available for epilepsy. These treatments require use of anti-seizure drugs but are not effective in controlling frequency of seizure. There is need of removal of an affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process, using EEG graphs, is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of an EEG signal in form of textual one-dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification of EEG data among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus, developed system will significantly help neurosurgeons in the increase of their performance.