Journal ArticleDOI
Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals
Abeg Kumar Jaiswal,Haider Banka +1 more
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TLDR
This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2017-04-01. It has received 139 citations till now. The article focuses on the topics: Linear classifier & Feature extraction.read more
Citations
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Journal ArticleDOI
Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
TL;DR: Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures.
1D-Local Binary Pattern Based Feature Extraction for Classification of Epileptic EEG Signals, Applied Mathematics and Computation243 (2014): 209-219.
TL;DR: An attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals, which could acquire high accuracy in classification of epileptic EEG signals.
Journal ArticleDOI
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
TL;DR: This paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics.
Journal ArticleDOI
Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals.
TL;DR: The clinical feasibility of the proposed seizure detection approach achieving superior performance over the cutting-edge techniques in terms of seizure detection performance and robustness is demonstrated.
Journal ArticleDOI
Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals
TL;DR: A combined method for multiclass learning and classification of various ExG biosignals such as electromyography, electroencephalography, and electrocorticography without requiring domain expert knowledge or ad hoc electrode selection process is described.
References
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Proceedings Article
A study of cross-validation and bootstrap for accuracy estimation and model selection
TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.
Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Journal ArticleDOI
Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state.
Ralph G. Andrzejak,Klaus Lehnertz,Florian Mormann,Christoph Rieke,Peter David,Christian E. Elger +5 more
TL;DR: Dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states are compared and strongest indications of nonlinear deterministic dynamics were found for seizure activity.
Journal ArticleDOI
EEG signal classification using wavelet feature extraction and a mixture of expert model
TL;DR: A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure and the results confirmed that the proposed Me network structure has some potential in detecting epileptic seizures.
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