Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

Abbaszadeh, Behrooz; Teixeira, César; Yagoub, M.C.E.

doi:10.2174/1874120702115010001

Cited by 4 publications

(1 citation statement)

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“…Detecting epileptic seizures with a focus on feature selection based on fuzzy membership was achieved in [ 22 ]. The authors in [ 23 ] conducted a comparative study to analyze discriminative features using various feature selection techniques in epilepsy. A method for EEG feature selection was introduced in [ 24 ] via stacked deep embedded regression with joint sparsity.…”

Section: Introductionmentioning

confidence: 99%

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy

Najafi

Jaafar

Remli

et al. 2022

Sensors

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Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy.

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Section: Introductionmentioning

confidence: 99%