Journal ArticleDOI
1D-local binary pattern based feature extraction for classification of epileptic EEG signals
TLDR
In this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented.About:
This article is published in Applied Mathematics and Computation.The article was published on 2014-09-01. It has received 223 citations till now. The article focuses on the topics: Feature extraction & Local binary patterns.read more
Citations
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Journal ArticleDOI
A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension
TL;DR: It appears that a system is in place to assist clinicians to diagnose seizures accurately in less time as the proposed model achieves perfect 100% classification sensitivity and is found to be outperforming all existing models in terms of classification sensitivity (CSE).
Journal ArticleDOI
Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform
TL;DR: In this paper, a system for epileptic seizure detection in electroencephalography (EEG) is described, which is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions.
Journal ArticleDOI
DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers
A. Sharmila,P. Geethanjali +1 more
TL;DR: The detection of an epileptic seizure based on DWT statistical features using naïve Bayes (NB) and k-nearest neighbor (k-NN) classifiers is more suitable in real time for a reliable, automatic epilepsy detection system to enhance the patient's care and the quality of life.
Journal ArticleDOI
Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals
TL;DR: The performance measure of the proposed multi-scale entropy measure has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.
Journal ArticleDOI
Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals
TL;DR: The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection and has been compared with existing methods for the classification of the aforementioned problems.
References
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Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Book
Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
Journal ArticleDOI
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
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.