What question did this study set out to answer?

The study aims to improve seizure detection accuracy in epilepsy through advanced machine learning techniques and feature extraction.

February 2, 2026

Optimized Seizure Detection in EEG Using Dual‐Branch Feature Fusion and Machine Learning Technique

Key Result

The proposed DBFFN feature extraction method achieved a 99.47% accuracy in seizure detection, outperforming existing techniques with improved sensitivity and specificity.

Key Points

The study aims to improve seizure detection accuracy in epilepsy through advanced machine learning techniques and feature extraction.
Implemented a multistage methodology for seizure detection.
Used CNN and RNN for robust EEG signal segmentation.
Extracted features with DBFFN and Hjorth parameters optimized by salp swarm and firefly algorithms.
Classified data using Naive Bayes and Random Forest.
Achieved an accuracy of 99.47% in seizure detection.
Sensitivity rate was 99.78% and specificity was 99.70%.
F1 score recorded at 99.51%, demonstrating superior performance compared to existing methods.

Structured PICO

Does DBFFN with dual metaheuristic optimization improve seizure detection accuracy in EEG recordings?

Population

EEG recordings for seizure detection in epilepsy

Intervention

Dual-Branch Feature Fusion Network (DBFFN) with dual metaheuristic optimization (SSO-FOA) and machine learning classification (Naive Bayes, Random Forest)

Comparator

Existing single-path or handcrafted feature-based methods

Outcome

Seizure detection accuracy, sensitivity, specificity, and F1 scoresurrogate

A novel dual-branch feature fusion network with machine learning classification achieves over 99% accuracy in detecting seizures from EEG recordings.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

ABSTRACT Epilepsy is a neurological disorder of the brain that generates seizures due to abnormal electrical activity. The diagnosis and management of the disease primarily depend on recordings of the EEG. A multistage methodology for seizure detection with enhanced accuracy and reliability is proposed in this work. Isolation of key components of the EEG signal is performed to carry out robust segmentation using CNN and RNN. Features will be extracted through DBFFN and Hjorth parameters, which obtain the optimal features after optimization through salp swarm optimization and firefly optimization algorithm. Data classification has been done using Naive Bayes and Random Forest with an accuracy of 99.47%, sensitivity of 99.78%, specificity of 99.70%, and an F1 score of 99.51%. Performances are higher in comparison with other techniques. DBFFN feature extraction methodology with firefly optimization was identified to be effective in enhancing seizure detection performance. Unlike existing single‐path or handcrafted feature‐based methods, the proposed DBFFN with dual metaheuristic optimization (SSO–FOA) jointly captures complementary spatial–temporal and spectral EEG features while reducing redundancy, resulting in superior accuracy, robustness, and computational efficiency. This computationally efficient system emerges as a reliable real‐world seizure detection tool in epilepsy management.

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