AUTOMATIC DETECTION OF EPILEPTIC EEG SIGNALS USING HIGHER ORDER CUMULANT FEATURES

2011 ◽  
Vol 21 (05) ◽  
pp. 403-414 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
JASJIT S. SURI
Keyword(s):  
Clinical Information ◽  
Epileptic Seizures ◽  
Epileptic Activity ◽  
Higher Order ◽  
Support Vector ◽  
P Value ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
The People ◽  
Eeg Recordings

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning

2021 ◽  
pp. 2150026
Author(s):  
Mehmet Akif Ozdemir ◽  
Ozlem Karabiber Cura ◽  
Aydin Akan
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Clinical Tool ◽  
Energy Distributions ◽  
Time Frequency ◽  
Synchrosqueezing Transform ◽  
Novel Method ◽  
Eeg Recordings ◽  
Accuracy Rates ◽  
Aided Diagnosis

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

scholarly journals Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Roozbeh Zarei ◽  
Jing He ◽  
Siuly Siuly ◽  
Guangyan Huang ◽  
Yanchun Zhang
Keyword(s):  
Principal Component ◽  
Epileptic Seizures ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Eeg Signals ◽  
Decision Tree Classifier ◽  
Efficient Detection ◽  
Eeg Data ◽  
Tree Classifier ◽  
Representative Samples

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study develops a new scheme based on Douglas-Peucker algorithm (DP) and principal component analysis (PCA) for extraction of representative and discriminatory information from epileptic EEG data. As the multichannel EEG signals are highly correlated and are in large volumes, the DP algorithm is applied to extract the most representative samples from EEG data. The PCA is utilised to produce uncorrelated variables and to reduce the dimensionality of the DP samples for better recognition. To verify the robustness of the proposed method, four machine learning techniques, random forest classifier (RF), k-nearest neighbour algorithm (k-NN), support vector machine (SVM), and decision tree classifier (DT), are employed on the obtained features. Furthermore, we assess the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that the DP technique effectively extracts the representative samples from EEG signals compressing up to over 47% sample points of EEG signals. The results also indicate that the proposed feature method with the RF classifier achieves the best performance and yields 99.85% of the overall classification accuracy (OCA). The proposed method outperforms the most recently reported methods in terms of OCA in the same epileptic EEG database.

scholarly journals Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

2022 ◽  
Vol 25 (1) ◽  
pp. 291
Author(s):  
Virupaxi Balachandra Dalal ◽  
Satish S. Bhairannawar
Keyword(s):  
Support Vector Machine ◽  
Adaptive Learning ◽  
Classification System ◽  
Training Data ◽  
Support Vector ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
The Neural Network ◽  
Eeg Data ◽  
Learning Technique

Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more </span>effectively.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350009 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
RATNA YANTI ◽  
JIA WEI ZHENG ◽  
M MUTHU RAMA KRISHNAN ◽  
JEN HONG TAN ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Texture Features ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Brain Disorder

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

ANALYSIS AND AUTOMATIC IDENTIFICATION OF SLEEP STAGES USING HIGHER ORDER SPECTRA

2010 ◽  
Vol 20 (06) ◽  
pp. 509-521 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
ERIC CHERN-PIN CHUA ◽  
KUANG CHUA CHUA ◽  
LIM CHOO MIN ◽  
TOSHIYO TAMURA
Keyword(s):  
Gaussian Mixture ◽  
Higher Order ◽  
Sleep Stages ◽  
P Value ◽  
Automatic Identification ◽  
Visual Interpretation ◽  
Eeg Signals ◽  
Visual Aid ◽  
Stage 1 ◽  
Higher Order Spectra

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.

A NEW TECHNIQUE FOR CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING HIGHER-ORDER SPECTRA

2019 ◽  
Vol 19 (01) ◽  
pp. 1940010 ◽  
Author(s):  
RAHUL SHARMA ◽  
PRADIP SIRCAR ◽  
RAM BILAS PACHORI
Keyword(s):  
Epilepsy Surgery ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Community Health Care Centers ◽  
A New Technique ◽  
Data Reduction Technique ◽  
Higher Order Spectra ◽  
Health Care Centers

Epilepsy is a neurological disorder characterized by epileptic seizures inside the human brain. An authentic localization of epileptogenic area will help the clinicians for a successful epilepsy surgery. The epileptogenic area can be characterized by the focal electroencephalogram (EEG) signals. Hence, in this article, a bispectrum-based approach is implemented to characterize the focal EEG signals. The highest twenty-five magnitudes of bispectrum from the principal domain are used as features. The locality sensitive discriminant analysis (LSDA), data reduction technique, is implemented to reduce the number of attributes. The ranked LSDA attributes are input to the support vector machine (SVM) classifier yielding 96.2% classification accuracy using the entire Bern Barcelona EEG database. Hence, the proposed technique can be employed to confirm the epileptogenic area for successful epilepsy surgery and can be employed in the community health care centers and hospitals.

scholarly journals Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Shahab Abdulla ◽  
Mohammed Diykh ◽  
Sarmad K. D. Alkhafaji ◽  
Jonathan H. Greena ◽  
Hanan Al-Hadeethi ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Back Propagation ◽  
Epileptic Seizures ◽  
Back Propagation Neural Network ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Average Accuracy ◽  
Effective Selection ◽  
Kolmogorov Smirnov ◽  
Eeg Recordings

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

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