Work

Recurrence Quantification Analysis of EEG Signals for Automated Epileptic Seizure Detection Using Random Forest Classifier

Year: 2026

Type: article

Abstract: Epilepsy affects over 50 million people globally and its diagnosis relies heavily on visual inspection of electroencephalogram (EEG) recordings. This paper proposes an automated three-class EEG seizure detection system based on Recurrence Quantification Analysis (RQA), a nonlinear dynamical analysis technique that characterises the recurrence structure of phase space trajectories without stationarity assumptions. Six RQA features — Recurrence Rate (RR), Determinism (DET), Mean Diagonal Line Length (L_mean), Shannon Entropy (ENT), Laminarity (LAM), and Trapping Time (TT) — are extracted from the recurrence plot of each EEG epoch. A Random Forest classifier trained under 10-fold stratified cross-validation on the Bonn University EEG database achieves an accuracy of 96.67%, sensitivity of 96.67%, specificity of 98.33%, and F1-score of 96.65% for three-class (normal, interictal, ictal) classification. The proposed method outperforms published RQA-SVM and DWT-SVM baselines and requires no prior signal decomposition, making it computationally efficient for bedside monitoring.

Source: Iconic Research and Engineering Journals

DOI: 10.64388/irev9i9-1715295

Authors: Reeda Kunhimangalam, Sujith O K

Institution: Kannur Medical College

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W7140137749