Modeling of seizure and seizure-free EEG signals based on stochastic differential equations

Tajmirriahi, Mahnoosh; Amini, Zahra

doi:10.1016/j.chaos.2021.111104

Cited by 24 publications

(5 citation statements)

References 55 publications

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“…Details of previous studies on epilepsy detection using these datasets are summarized in Table 9 in comparison with the framework proposed in this paper. Evidently, seizures could be detected efficiently by all the methods listed, with classification accuracies of more than 0.9, and the proposed framework outperformed several existing models [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. However, few studies showed better classification results than that achieved in the present study, possibly owing to the selection of non-seizure data, the selection of data sample size, the difference in data preprocessing, the difference in the applied method applied, the difference in classifier, etc.…”

Section: Discussionmentioning

confidence: 87%

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Wang

Shi

et al. 2022

Entropy

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Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and am optimization algorithm for feature selection.

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Section: Discussionmentioning

confidence: 87%

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Wang

Shi

et al. 2022

Entropy

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“…The documented accuracy for each is 96.38, 100, and 97.15%. Due to its inherent self-similarity, Tajmirriahi and Amini ( 2021 ) used stochastic differential equations (SDEs) to model EEG signals with self-similar fractional Levy stabilization processes. They Fit the probability distribution to the derived EEG signal histogram, and extracted the parameters of the fitted histogram.…”

Section: Discussionmentioning

confidence: 99%

Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers

Zeng

et al. 2023

Front. Neurosci.

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IntroductionIn the clinical setting, it becomes increasingly important to detect epileptic seizures automatically since it could significantly reduce the burden for the care of patients suffering from intractable epilepsy. Electroencephalography (EEG) signals record the brain's electrical activity and contain rich information about brain dysfunction. As a non-invasive and inexpensive tool for detecting epileptic seizures, visual evaluation of EEG recordings is labor-intensive and subjective and requires significant improvement.MethodsThis study aims to develop a new approach to recognize seizures automatically using EEG recordings. During feature extraction of EEG input from raw data, we construct a new deep neural network (DNN) model. Deep feature maps derived from layers placed hierarchically in a convolution neural network are put into different kinds of shallow classifiers to detect the anomaly. Feature maps are reduced in dimensionality using Principal Component Analysis (PCA).ResultsBy analyzing the EEG Epilepsy dataset and the Bonn dataset for epilepsy, we conclude that our proposed method is both effective and robust. These datasets vary significantly in the acquisition of data, the formulation of clinical protocols, and the storage of digital information, making processing and analysis challenging. On both datasets, extensive experiments are performed using a cross-validation by 10 folds strategy to demonstrate approximately 100% accuracy for binary and multi-category classification.DiscussionIn addition to demonstrating that our methodology outperforms other up-to-date approaches, the results of this study also suggest that it can be applied in clinical practice as well.

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“…In [ 99 ], SPD was utilized and shown to be less prone to noise and outliers while being able to identify seizures with high accuracy. In [ 100 ], the authors combined the capabilities of Riemannian geometry and fractals using the Riemann–Liouville fractional derivative (RLFD) operator to exploit the EEG signal in the continuous-time domain, resulting in special models for healthy and epileptic patients. Ictal events were characterized by histograms with heavy-tailed distributions.…”

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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Modeling of seizure and seizure-free EEG signals based on stochastic differential equations

Cited by 24 publications

References 55 publications

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers

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

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