Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

2014 ◽  
Vol 113 (2) ◽  
pp. 494-502 ◽  
Author(s):  
Ram Bilas Pachori ◽  
Shivnarayan Patidar
Keyword(s):  
Epileptic Seizure ◽  
Second Order ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Order Difference ◽  
Seizure Classification ◽  
Difference Plot

Analysis of eyes open, eye closed EEG signals using second-order difference plot

2007 ◽  
Vol 45 (12) ◽  
pp. 1243-1249 ◽  
Author(s):  
Ranjit A. Thuraisingham ◽  
Yvonne Tran ◽  
Peter Boord ◽  
Ashley Craig
Keyword(s):  
Second Order ◽  
Eeg Signals ◽  
Order Difference ◽  
Eyes Open ◽  
Difference Plot

scholarly journals An Improved Optimization Algorithm for Epileptic Seizure Detection in EEG Signals Using Random Forest Classifier

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 327-340
Author(s):  
A. Phraeson Gini ◽  
Dr.M.P. Flower Queen
Keyword(s):  
Random Forest ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
True Positive Rate ◽  
Intrinsic Mode Functions ◽  
Selection Algorithm ◽  
Eeg Signals ◽  
Efficient Detection ◽  
Positive Rate ◽  
Electroencephalogram Eeg

Epilepsy is a psychiatric condition that has serious consequences for the human brain. The Electroencephalogram (EEG) may reveal a pattern that tells physicians whether an epileptic seizure is likely to occur again. EEG testing may also help the physician exclude other conditions that mimic epilepsy as a reason for the seizure. Now-a-days the researchers are showing much interest in these seizure detection because of its significance in epileptic detection. This paper is addressing an efficient soft computing framework for seizure detection from the EEG signal. The proposed pipeline of work is having the state-of-art as the possibility of achieving the maximum accuracy. The spectral features extracted from the Intrinsic mode functions (IMF) of EEG samples and it is directing the proposed flow towards the efficient detection of seizure and also the random forest algorithm based a convulsion classification is reliable for because of its learning behavior from the huge number of known dataset. The feature selection algorithm in this proposed work is stimulating the overall work towards the maximum true positive rate. This work is implemented on MATLAB platform and dataset were downloaded from the universal database such as Bonn university database. The results obtained from the proposed approach is showing the truthfulness of the approach introduced here.

Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

2020 ◽  
Vol 65 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Chahira Mahjoub ◽  
Régine Le Bouquin Jeannès ◽  
Tarek Lajnef ◽  
Abdennaceur Kachouri
Keyword(s):  
Epileptic Seizure ◽  
High Performance ◽  
Visual Analysis ◽  
Binary Classification ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Epileptic Seizure Detection

AbstractElectroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.

Deep learning with 3D-second order difference plot on respiratory sounds

2018 ◽  
Vol 45 ◽  
pp. 58-69 ◽  
Author(s):  
Gokhan Altan ◽  
Yakup Kutlu ◽  
Adnan Özhan Pekmezci ◽  
Serkan Nural
Keyword(s):  
Deep Learning ◽  
Second Order ◽  
Respiratory Sounds ◽  
Order Difference ◽  
Difference Plot
Sign in / Sign up

Export Citation Format

Share Document