"A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine"
written by Yuedong Song, Pietro Liò,
published by Journal of Biomedical Science and Engineering, Vol.3 No.6, 2010
has been cited by the following article(s):
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[1] Research Review an Automatic Detection of Epilepsy in Human brain signal
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[2] Unsupervised EEG feature extraction based on echo state network
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[3] Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals
[4] Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor
[5] Automated Epileptic Seizure Detection Method Based on the Multi-attribute EEG Feature Pool and mRMR Feature Selection Method
[6] Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification
[7] Formulation of a Novel Classification Indices for Classification of Human Hearing Abilities According to Cortical Auditory Event Potential signals
[8] Epileptic Seizure Detection and Classification Using Machine Learning
[9] EEG-based single-channel authentication systems with optimum electrode placement for different mental activities
[10] Visual seizure annotation and automated seizure detection using behind-the-ear EEG channels
[11] Automatic Detection of Epileptic Seizure Based on Approximate Entropy, Recurrence Quantification Analysis and Convolutional Neural Networks
[12] Machine Learning Methods for EEG-based Epileptic Seizure Detection
[13] Time-time analysis of electroencephalogram signals for epileptic seizure detection
[14] Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: review of available methodologies
[15] Automatic Detection of Epileptic Spikes in Intracerebral EEG with Convolutional Kernel Density Estimation
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[16] A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation
[17] Epileptic Seizure detection with Permutation Fuzzy Entropy using robust machine learning techniques.
[18] Performance Analysis of Fuzzy Multilayer Support Vector Machine for Epileptic Seizure Disorder Classification using Auto Regression Features
[19] Application of Clustering Techniques on Statistical Features of EEG Signals for Seizure Detection.
[20] Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines
[22] A support vector machine approach for AF classification from a short single-lead ECG recording
[23] Imbalance Learning Using Neural Networks for Seizure Detection
[24] Robust Detection of Epileptic Seizures Using Deep Neural Networks
[25] Brain Data Mining for Epileptic Seizure-Detection
[26] Epileptic Seizure Detection Using Empirical Mode Decomposition Based Fuzzy Entropy and Support Vector Machine
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[27] Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine
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[28] Virtual Tai-Chi System: A smart-connected modality for rehabilitation
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[29] Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals
Expert Systems with Applications, 2018
[30] Epileptic Seizure Detection: A Deep Learning Approach
[31] Topolnogical classifier for detecting the emergence of epileptic seizures
[32] Multiresolution analysis on nonlinear complexity measurement of EEG signal for epileptic discharge monitoring
[33] A support vector machine approach for AF classification from a short single lead ECG recording
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[34] VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability
[35] Epileptic Seizure Detection in Long-Term EEG Recordings by Using Wavelet-Based Directed Transfer Function
[36] High performance EEG feature extraction for fast epileptic seizure detection
[37] Developing enhanced classification methods for ECG and EEG signals
[38] Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery
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[39] Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique
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[40] The Feature Extraction Method of EEG Signals Based on Transition Network
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[41] The Feature Extraction Method of EEG Signals Based on the Loop Coefficient of Transition Network
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[42] The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders.
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[43] Using EEG Data Analytics to Measure Meditation
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[44] Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface
[45] Application of Convex Optimization Techniques for Feature Extraction from EEG Signals
[46] The potential application of multiscale entropy analysis of electroencephalography in children with neurological and neuropsychiatric disorders
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[47] Focal and Non-Focal EEG Signal Classification by Computing Area of 2D-PSR Obtained for IMF
[48] A novel smoothness-based interpolation algorithm for division of focal plane Polarimeters
[49] Analysis of PAC Learning Based Bayesian Optimization with Autoencoders for Epilepsy Classification from EEG Signals
[51] Hyperspectral image band selection via global optimal clustering
[53] Automated diagnosis of Epilepsy from EEG signals using Ensemble Learning approach
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[54] Altered resting-state EEG complexity in children with Tourette syndrome: A preliminary study.
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[55] Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG
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[56] Effects of force load, muscle fatigue and extremely low frequency magnetic stimulation on EEG signals during side arm lateral raise task
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[57] Automatic epileptic EEG detection using DT-CWT-based non-linear features
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[58] Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals
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[59] The distortion of data compression via compressed sensing in EEG telemonitoring for the epileptic
[60] A quick approach to detect epilepsy and seizure in brain
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[61] Topological classifier for detecting the emergence of epileptic seizures
[62] Efficient feature extraction framework for EEG signals classification
[63] 基于小波包节律和支持向量机的警戒低觉醒脑电信号识别方法
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[64] Time-Frequency Based Methods for Non-Stationary Signal Analysis with Application To EEG Signals
[65] Principal dynamic mode analysis of neural mass model for the identification of epileptic states
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[66] Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM
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[67] Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals
[68] Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis
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[69] Classifying Driving Fatigue Based on Combined Entropy Measure Using EEG Signals
[70] Detection of epileptic seizure in EEG signals using linear least squares preprocessing
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[71] The Diagnosis of Epilepsy by Gravitational Search Algorithm and Support Vector Machines
[72] A Real Time EEG Analysis System
[73] Research Article Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
[74] Application of entropies for automated diagnosis of epilepsy using EEG signals
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[75] Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements
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[76] Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy
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[77] Regularized online sequential extreme learning machine with adaptive regulation factor for time-varying nonlinear system
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[78] Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy
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[79] Application of entropies for automated diagnosis of epilepsy using EEG signals: A review
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[80] Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis
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[82] Automated Detection of Central Apnea in Preterm Infants
[83] Statistical Machine Learning in Brain State Classification using EEG Data
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[85] GMM better than SRC for classifying epilepsy risk levels from EEG signals
[86] EEG signals classification based on wavelet packet and ensemble Extreme Learning Machine
[87] Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
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[89] Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
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[90] Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means
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[91] Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals
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[92] A machine learning system for automated whole-brain seizure detection
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[93] A Novel Feature Extraction Method for Epileptic EEG Based on Degree Distribution of Complex Network
[94] Multi-scale sample entropy as a feature for working memory study
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[95] Novel feature extraction method based on weight difference of weighted network for epileptic seizure detection
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[96] Comparison of classification methods on EEG signals based on wavelet packet decomposition
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[97] Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation
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[99] Investigating the impacts of epilepsy on EEG-based person identification systems
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[100] Feature Extraction Method for Epileptic Seizure Detection Based on Cluster Coefficient Distribution of Complex Network
[101] 基于递归量化分析与支持向量机的癫痫脑电自动检测方法
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[102] Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM
[103] Caracterización de medidas de regularidad en se?ales biomédicas. Robustez a outliers
[104] Empirical Mode Decomposition Based Classification of Focal and Non-focal Seizure EEG Signals
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[105] Detection of Epileptic Seizure Event and Onset Using EEG
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[106] Design and Development of Prediction Model to Detect Seizure Activity Utilizing Higher Order Statistical Features of EEG signals
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[107] The neoteric feature extraction method of epilepsy EEG based on the vertex strength distribution of weighted complex network
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[108] Multiscale sample entropy for time resolved epileptic seizure detection and fingerprinting
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[110] Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm
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[111] Using Shannon Entropy as EEG Signal Feature for Fast Person Identification
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[112] Performance Analysis of Extreme Learning Machine for Robust Classification of Epilepsy from EEG Signals
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[113] A novel method for analysis of EEG background activity in epileptic patients and healthy subjects using Hilbert transform
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[114] Caracterización de medidas de regularidad en señales biomédicas. Robustez a outliers
[115] Analysis of EEG signals using complex brain networks
[116] Analysis of electroencephalogram background activity in epileptic patients and healthy subjects using dispersion entropy
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[117] Human seizure detection using quadratic Renyi entropy
[118] Automatic Seizure Detection Using EEG
[119] 西安交通大学学报 (未开通)
[120] Detection of epileptic seizure in EEG recordings by spectral method and statistical analysis
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[121] 脑卒中后抑郁症静息脑电信号非线性特征提取与分析
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[122] A novel approach for lie detection based on F-score and extreme learning machine
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[123] Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm
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[124] Human seizure detection using quadratic Rényi entropy
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[125] Automatic Seizure Detection using Inter Quartile Range
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[126] Epileptic seizure detection using wavelet transform based sample entropy and support vector machine
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[127] Multi-wavelet transform based epilepsy seizure detection
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[128] 基于概率判决极端学习机的癫痫发作预报研究
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[129] Automated diagnosis of epileptic EEG using entropies
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[130] Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals
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[131] Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine
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[132] Automated diagnosis of normal and alcoholic EEG signals
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[133] EEG signal classification using empirical mode decomposition and support vector machine
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[134] Epileptic EEG classification based on extreme learning machine and nonlinear features
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[135] Methodology for epileptic episode detection using complexity-based features
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[136] Automatic Seizure Detection Based on Wavelet-Chaos Methodology from EEG and its Sub-bands
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[137] Application of the Sample Entropy for Discrimination between Seizure and Seizure-Free EEG Signals.
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[138] Unsupervised Classification of Epileptic EEG Signals