# Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

arXiv: https://arxiv.org/abs/2109.04364 · HTML: https://mlsanity.com/paper/2109.04364/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 185 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Epileptic Seizures Detection Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2007.01276/) (2020)
- [A review of feature extraction and performance evaluation in epileptic seizure detection using EEG](https://mlsanity.com/paper/1908.00492/) (2019)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models](https://mlsanity.com/paper/2109.01120/) (2021)
- [Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)](https://mlsanity.com/paper/2008.10114/) (2020)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks](https://mlsanity.com/paper/1901.05894/) (2019)
- [Material Recognition for Automated Progress Monitoring using Deep Learning Methods](https://mlsanity.com/paper/2006.16344/) (2020)
- [Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy](https://mlsanity.com/paper/1903.08100/) (2019)
- [Modified Jaccard Index Analysis and Adaptive Feature Selection for Location Fingerprinting with Limited Computational Complexity](https://mlsanity.com/paper/1901.03328/) (2019)

## Built on by (most cited first, 9 of 9)

- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.14611/) (2022)
- [MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG](https://mlsanity.com/paper/2211.04628/) (2022)
- [SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder](https://mlsanity.com/paper/2406.17537/) (2024)
- [What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors](https://mlsanity.com/paper/2109.05457/) (2021)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [A Dynamic Fuzzy Rule and Attribute Management Framework for Fuzzy Inference Systems in High-Dimensional Data](https://mlsanity.com/paper/2504.19148/) (2025)

## Abstract (arXiv)

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them.