# Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis

arXiv: https://arxiv.org/abs/2003.04534 · HTML: https://mlsanity.com/paper/2003.04534/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

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

- [Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human vision](https://mlsanity.com/paper/2309.03590/) (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)
- [Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories](https://mlsanity.com/paper/2310.01416/) (2023)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Neural Network Based Epileptic EEG Detection and Classification](https://mlsanity.com/paper/2111.03268/) (2021)
- [Automated Human Mind Reading Using EEG Signals for Seizure Detection](https://mlsanity.com/paper/2111.03270/) (2021)
- [Image-based Data Representations of Time Series: A Comparative Analysis in EEG Artifact Detection](https://mlsanity.com/paper/2401.05409/) (2023)
- [Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field](https://mlsanity.com/paper/2110.12503/) (2021)

## Abstract (arXiv)

This paper evaluates the approach of imaging timeseries data such as EEG in the diagnosis of epilepsy through Deep Neural Network (DNN). EEG signal is transformed into an RGB image using Gramian Angular Summation Field (GASF). Many such EEG epochs are transformed into GASF images for the normal and focal EEG signals. Then, some of the widely used Deep Neural Networks for image classification problems are used here to detect the focal GASF images. Three pre-trained DNN such as the AlexNet, VGG16, and VGG19 are validated for epilepsy detection based on the transfer learning approach. Furthermore, the textural features are extracted from GASF images, and prominent features are selected for a multilayer Artificial Neural Network (ANN) classifier. Lastly, a Custom Convolutional Neural Network (CNN) with three CNN layers, Batch Normalization, Max-pooling layer, and Dense layers, is proposed for epilepsy diagnosis from GASF images. The results of this paper show that the Custom CNN model was able to discriminate against the focal and normal GASF images with an average peak Precision of 0.885, Recall of 0.92, and F1-score of 0.90. Moreover, the Area Under the Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve is 0.92 for the Custom CNN model. This paper suggests that Deep Learning methods widely used in image classification problems can be an alternative approach for epilepsy detection from EEG signals through GASF images.