Analysis of EEG signals for the detection of epileptic seizures using feature extraction

Kandagatla, Ravi Kumar; Naidu, V. Jayachandra; Reddy, P.S. Sreenivasa; Pandilla, Veera Kavya; Joshitha, Marriwada; Rakesh, Chanamala

VOLUME 17 (Supplement)

SciEnggJ%202024%20Special%20Issue%201 7 Pasham%20et%20al

SciEnggJ 17 (Supplement) 028-035
available online: February 13, 2024
DOI: https://doi.org/10.54645/202417SupCQP-72

*Corresponding author
Email Address: 2k6ravi@gmail.com
Date received: May 6, 2023
Date revised: December 30, 2023
Date accepted: December 31, 2023

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ARTICLE

Analysis of EEG signals for the detection of epileptic seizures using feature extraction

Ravi Kumar Kandagatla*¹, V. Jayachandra Naidu², P.S. Sreenivasa Reddy³, Veera Kavya Pandilla¹, Marriwada Joshitha¹, and Chanamala Rakesh¹

¹Department of Electronics and Communication Engineering, Lakireddy Bali
     Reddy College of Engineering (Autonomous), Mylavaram-521230,
     Andhra Pradesh, India
²Department of Electronics and Communication Engineering,
     Sri Venkateswara College of Engineering & Technology (Autonomous),
     Chittoor, India
³Department of Electronics and Communication Engineering,
     Nalla Narasimha Reddy Education Society’s group of Institutions,
     Telangana, India

KEYWORDS: Epileptic Seizure, Feature Extraction, Machine Learning, Classification, Electrical Signals, Stochastic Gradient Descent, K-Nearest Neighbors Algorithm, Naive Bayes, Decision Tree, Logistic Regression, Extra Tree Classifier, Accuracy.

The electroencephalogram, which tracks electrical signals in the central nervous system, has been extensively used to diagnose epilepsy, which represents a particular sort of brain abnormality. However, developing seizure classification techniques with significantly better precision and reduced complexity remains challenging. The Epileptic Seizure Recognition dataset, which is publicly accessible in the Kaagle and in the machine learning repository, was used to identify seizures. To identify the seizure, we compared six classification methods to determine which one had the highest success rate. The dataset is subsequently divided, trained, and tested in order to categorize it further using six machine learning algorithms: Stochastic Gradient Descent, Logistic Regression, Naïve Bayes, K-Nearest Neighbors Algorithm, Extra Tree Classifier and Decision Tree. When contrasted with alternative techniques, Extra Trees Classifier possesses the highest accuracy results. The algorithm attained a 96 percent success rate.