A combination of statistical parameters for epileptic seizure detection and classification using VMD and NLTWSVM

Zhang, Shang; Liu, Guangda; Xiao, Ruolan; Cui, Wenjie; Cai, Jing; Hu, Xinlei; Qiu, Jiqing; Yuan, Qi

doi:10.1016/j.bbe.2022.02.004

Cited by 22 publications

(6 citation statements)

References 69 publications

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“… Jia et al (2022) developed variable weight convolutional neural networks (VWCNNs) for seizure classification, attaining an accuracy of 91.71% and a weighted F1 of 94.00%. Zhang et al (2022) implemented a variational mode decomposition (VMD) technique and nonlinear twin support vector machine (NLTWSVM), recording an accuracy of 92.29% and weighted F1 of 92.30%. Li et al (2022) utilized fast Fourier transform (FFT) and a graph-generative neural network (GGN) for dynamic brain functional connectivity analysis, holding out (HO) 2/3 of EEG data for training and 1/3 for testing, achieving 91.00% accuracy and weighted F1.…”

Section: Discussionmentioning

confidence: 99%

Residual and bidirectional LSTM for epileptic seizure detection

Zhao,

Wang,

Patnaik

et al. 2024

Front. Comput. Neurosci.

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Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88–100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.

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Section: Discussionmentioning

confidence: 99%

Residual and bidirectional LSTM for epileptic seizure detection

Zhao,

Wang,

Patnaik

et al. 2024

Front. Comput. Neurosci.

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“…The researchers reported 100% Acc, Sn and Sp for the multiclass epilepsy classification from EEG signals (healthy, ictal, inter-ictal) [ 59 , 60 ]. Other scientists used a method based on variational mode decomposition (VMD) and a non-linear twin support vector machine (NLTWSVM) for epileptic seizure detection and classification [ 61 ]. The authors reported 99.2% Sn, 99.5% Sp and 99.4% Acc for differentiation between non-seizure and seizure EEG records of a publicly available online database from the Department of Epileptology, University of Bonn, Germany [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Statsenko,

Babushkin,

Talako

et al. 2023

Biomedicines

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Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

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“…Statistical properties, such as mean, median, variance, standard deviation, skewness, kurtosis, peak amplitude, minimum amplitude, peak to peak, and similar, are the simplest features that may be derived from an EEG signal in the time domain [ 22 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Hjorth parameters are based on the variance of the subsequent derivatives of the EEG signal.…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.

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A combination of statistical parameters for epileptic seizure detection and classification using VMD and NLTWSVM

Cited by 22 publications

References 69 publications

Residual and bidirectional LSTM for epileptic seizure detection

Residual and bidirectional LSTM for epileptic seizure detection

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

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