Journal ArticleDOI
A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension
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TLDR
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).About:
This article is published in Pattern Recognition Letters.The article was published on 2017-07-15. It has received 308 citations till now. The article focuses on the topics: Wavelet transform & Ictal.read more
Citations
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Journal ArticleDOI
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli +6 more
TL;DR: In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
Journal ArticleDOI
An automated system for epilepsy detection using eeg brain signals based on deep learning approach
TL;DR: In this article, an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models is proposed to detect ternary cases e.g. normal vs. interictal.
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
Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals
TL;DR: The aim is to comprehensively investigate the channel specific nature of EEG signals and to provide an effective method based on flexible analytic wavelet transform (FAWT) for recognition of emotion and to show better performance for human emotion classification.
Posted Content
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
TL;DR: A system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models, which results in 60% fewer parameters compared to traditional CNN models, to overcome the limitations of small amount of data.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
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