Improved Patient-Independent Seizure Detection System Using Novel Feature Extraction Techniques

Nandini, Durgesh; Yadav, Jyoti; Rani, Asha; Singh, Vijander

doi:10.1007/978-981-16-3346-1_71

Cited by 5 publications

(2 citation statements)

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“…Genetic algorithm and particle swarm optimization were used to refine the parameters of hybrid SVM in [26] for the Bonn dataset. In [17], two time-domain feature extraction methods were utilized along with different classifiers like SVM, K nearest neighbors (KNN), logistic regression (LR), RF, decision tree (DT), naive Bayes (NB), etc., to detect seizure. The feedforward NN was used in [14] to classify seizures, where wavelet decomposition was used along with feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies have focused on using raw EEG signals (e.g., [4][5][6][11][12][13]) and employed DL learning methods for feature extraction with embedded methods of individual DL methods. Furthermore, recent studies have explored various feature extraction methods to capture discriminative information from raw EEG signals [3,11,[14][15][16][17]. Recent EEG-based ES studies have focused on signal transformation and feature extraction techniques to achieve better ES recognition using ML/DL methods.…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network

Das,

Mumu,

Akhand

et al. 2024

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Electroencephalogram (EEG) has emerged as the most favorable source for recognizing brain disorders like epileptic seizure (ES) using deep learning (DL) methods. This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes EEG signals into six intrinsic mode functions (IMFs). Three distinct features, namely, fluctuation index, variance, and ellipse area of the second order difference plot (SODP), were extracted from each of the IMFs. The feature values from all EEG channels were arranged in two composite feature forms: a 1D (i.e., unidimensional) form and a 2D image-like form. For ES recognition, the convolutional neural network (CNN), the most prominent DL model for 2D input, was considered for the 2D feature form, and a 1D version of CNN was employed for the 1D feature form. The experiment was conducted on a benchmark CHB-MIT dataset as well as a dataset prepared from the EEG signals of ES patients from Prince Hospital Khulna (PHK), Bangladesh. The 2D feature-based CNN model outperformed the other 1D feature-based models, showing an accuracy of 99.78% for CHB-MIT and 95.26% for PHK. Furthermore, the cross-dataset evaluations also showed favorable outcomes. Therefore, the proposed method with 2D composite feature form can be a promising ES detection method.

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Section: Related Workmentioning

confidence: 99%