scholarly journals Epileptic Seizures Detection Based on Empirical Mode Decomposition of EEG Signals

10.5772/18302 ◽  
2011 ◽  
Author(s):  
Lorena Orosco ◽  
Agustina Garces ◽  
Eric Laciar
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizures ◽  
Eeg Signals ◽  
Mode Decomposition

EMPIRICAL MODE DECOMPOSITION-BASED PROCESSING FOR AUTOMATED DETECTION OF EPILEPSY

2019 ◽  
Vol 19 (01) ◽  
pp. 1940003 ◽  
Author(s):  
G. MURALIDHAR BAIRY ◽  
YUKI HAGIWARA
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizures ◽  
Automated Detection ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Small Magnitude ◽  
Cad System ◽  
Mode Decomposition ◽  
Non Linear ◽  
The Brain

Epilepsy is a chronic illness of the brain characterized by recurring seizure attacks. Electroencephalogram (EEG) can record the electrical activity of the brain and is extensively used to analyze and diagnose epileptic seizures. However, the EEG signals are highly non-linear and chaotic and are difficult to analyze due to their small magnitude. Hence, empirical mode decomposition (EMD), a non-linear technique, has been widely adopted to capture the subtle changes present in the EEG signals. Hence, it is an added advantage to develop an automated computer-aided diagnostic (CAD) system to detect the different brain activities from the EEG signals using machine learning approaches. In this paper, we focus on the previous works which have used the EMD technique in the automated detection of normal or epileptic EEG signals.

scholarly journals Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 140 ◽  
Author(s):  
Jiang Wu ◽  
Tengfei Zhou ◽  
Taiyong Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizures ◽  
Ensemble Empirical Mode Decomposition ◽  
Gradient Boosting ◽  
Massachusetts Institute Of Technology ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
End Effects ◽  
Mode Decomposition ◽  
Extreme Gradient Boosting

Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (IMFs) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children’s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

scholarly journals Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study

2018 ◽  
Vol 12 ◽  
Author(s):  
Pablo Andrés Muñoz-Gutiérrez ◽  
Eduardo Giraldo ◽  
Maximiliano Bueno-López ◽  
Marta Molinas
Keyword(s):  
Comparative Study ◽  
Empirical Mode Decomposition ◽  
Eeg Signals ◽  
Mode Decomposition
Sign in / Sign up

Export Citation Format

Share Document