EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures

Wang, Lei; Long, Xi; Arends, Johan; Aarts, Ronald M.

doi:10.1016/j.jneumeth.2017.07.013

Cited by 51 publications

(33 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The eigenvector strategies, for example, minimum-norm and multiple signal classification (MUSIC) are most appropriate to the signals that can be made of few sinusoids covered in noise 21 . Recently, the feature extraction techniques are combined with various classifiers like: adaptive neuro-fuzzy inference system 39 , support vector machine (SVM) 35 , Global modular PCA with SVM 41 , least square support vector machine (LS-SVM) 45 and artificial neural network (ANN) 31,39 , ANN with Fuzzy relations 32 , multilayer perceptron neural network (MLPNN) 42 , recurrent neural network (RNN) 39 , relevance vector machine (RVM), probabilistic neural network (PNN), mixture of experts (MEs), modified mixture of experts (MMEs), k-NN 15,34 , Genetic algorithm 38 , nonlinear sparse extreme learning machine 43 , Wavelet based envelope analysis (EA) with neural network ensemble 20 , random forest classifier 22,16 , Bayesian classifier 23 , fuzzy entropy model 24 , rule based classifier 26 , weighted extreme learning 13 and logistic tree model. The execution of a classifier depends on the qualities of the classified data.…”

Section: Eeg Signal Classification Methodsmentioning

confidence: 99%

“…For instance, the issue with binary classification involves the separation of classes into two category, for example, the target and non-target classes. Classification algorithms rely mostly on labelled output, where the learning is supervised or unsupervised based on statistical or non-statistical data.The supervised classification algorithms foreseescategorical labels as: support vector machine (SVM) [26][27][28][29][30][31][32][33][34][35] , Global modular PCA with SVM [36][37][38][39][40][41] , linear discriminant analysis (LDA), Naive Bayes, decision trees, K-nearest-neighbour (kNN), logistic regression, neural networks, Kernel estimation, linear regression, Kalman filters, Gaussian process regression, fractional linear prediction 2 etc. The aim of these algorithms is to amplify the precision of testing over testing dataset and hence, the supervised algorithms are used mostly in classifying the EEG signals.…”

Section: Eeg Signal Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Review on diverse approaches used for epileptic seizure detection using EEG signals

Baskar

Karthikeyan

2018

Bangladesh J Med Sci

View full text Add to dashboard Cite

Epileptic seizure detection is a common diagnosis practiced by the expert clinicians through direct visual observation from the electroencephalography (EEG) signal. This detection by the expert clinicians is considered sensitive to bias and time consuming. Further, it suffers from various problems like unsustainability in larger dataset processing and low power detection. Hence, many computerized detection approaches are highly preferred to eliminate the aforementioned problems and to expedite the research in epilepsy seizure detection for aiding the medical professionals. Many such automated epilepsy diagnosis framework has been designed by various researches, which is made to operate in a single or in a combined manner with other domains. This study reviews different approaches, which is been designed to aid the human diagnosis using new avenues that explains the causes of epilepsy and seizures. Further, this study summarizes various methods used previously to analyze the epilepsy and seizures based on its state of art approach. Also, investigations are carried out in terms of performance evaluation to find the best suitable epileptic seizure detection technique in the application of Neuro-informatics.Bangladesh Journal of Medical Science Vol.17(4) 2018 p.526-531

show abstract

Section: Eeg Signal Classification Methodsmentioning

confidence: 99%

Section: Eeg Signal Classificationmentioning

confidence: 99%

Review on diverse approaches used for epileptic seizure detection using EEG signals

Baskar

Karthikeyan

2018

Bangladesh J Med Sci

View full text Add to dashboard Cite

show abstract

“…The modularity of transferred network outperformed other network characteristics. Wang et al [ 101 ] studied the EEG seizure patterns’ influence on detection performance. A visibility graph algorithm and two derivatives are applied on EEGs recorded from epileptic patients with intellectual disability.…”

Section: Complexity In Epileptic Seizure Monitoringmentioning

confidence: 99%

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Mei

Xian

Chen

et al. 2018

Sensors

View full text Add to dashboard Cite

Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.

show abstract

“…Lei Wang et al [36] used visibility graphs to analyse seizure patterns in EEG signals. By calculating and comparing degree distributions they manage to show that it can be used to discern between EEG recording with and without seizures.…”

Section: Introductionmentioning

confidence: 99%

Identification of focal epileptic regions from electroencephalographic data: Feigenbaum graphs

et al. 2021

View full text Add to dashboard Cite

In the study of problems related to epilepsy analyzing electroencephalograms data is of much importance to help, one hand, to its diagnosis, and, another hand in the possibility of diminishing errors in surgery. We do this analysis making the Feigenbaum graphs for real electroencephalographic signals data sets and calculating characteristic networks (graph) quantities, such as average clustering, degree distribution, and average shortest path length. We manage to characterize two different data sets from each other, from data sets corresponding to focal and non-focal neuronal activity both time out of an epileptic seizure. This method enables us to identify sets of data from epileptic focal zones and suggest our approach could be used to aid physicians with diagnosing epilepsy from electroencephalographic data and/or in an exact establishment of the epileptic focal region for surgery.

show abstract

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures

Abstract: The proposed VGS-based features can help improve seizure detection for ID patients.

Cited by 51 publications

References 48 publications

Review on diverse approaches used for epileptic seizure detection using EEG signals

Review on diverse approaches used for epileptic seizure detection using EEG signals

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Identification of focal epileptic regions from electroencephalographic data: Feigenbaum graphs

Contact Info

Product

Resources

About