Analogy of Algorithms for Automatic Epileptic Seizure Detection

Kavya, B.; Prasad, S. N.

doi:10.1109/rteict49044.2020.9315627

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Using DWT, EEG signals are broken down into five sub bands of frequency. The wavelet energy distribution at each sub-band level is also the main parameter for detecting convulsions to build up a series of functions (7). Deep learning algorithms were used by the author for seizure prediction research.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization of electroencephalograph (EEG) Signals for Epilepsy Seizure Prediction by utilizing VSPO Genetic Algorithms with SVM and Machine Learning Methods

Banupriya¹,

Devi²

2021

IJST

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Objectives: To optimize the EEG signals in order to predict the epileptic seizures at early stage and to improve the accuracy level by employing genetic algorithm and machine learning methods. Methods: Virus Swarm Particle Optimization Technique (VSPO) based Genetic algorithm is utilized for the purpose of feature selection and Machine Learning SVM technique is utilized for classification of EEG signals to determine seizure or non-seizure. The Discrete Wavelet Transform (DWT) is utilized for factor extraction to assess the recurrence range of EEG signals associated with seizures, to partition them into separate spaces using DWT of EEG symbols, and to consider the variations between seizure and normal functionality. VPSO-GA with SVM extracts the features from the Andrzejak R G dataset and then selects the relevant function to perform classification and prediction in order to optimize the EEG signals for early ES prediction and to improve the accuracy level. To demonstrate the effectiveness of the proposed algorithm, MATLAB is used for implementation. The performance results are compared to the existing baseline versions FCM-MPSO, EDMLC and K-MODE. Findings: EEG signals are optimized and early ES prediction is done with 98.13% accuracy level, 98.03% sensitivity, 98.01% specificity, 98.90% Precision, 97.96% Recall, 191 True Positive, 104 True Negative and 98.46% F-Score to predict the seizure in an optimized manner which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed algorithm VPSO-SVM outperforms FCM-MPSO, EDMLC and K-MODE in terms of accuracy level of epileptic seizure prediction at early stage by optimizing the EEG signals in a robust manner.

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Section: Introductionmentioning

confidence: 99%