Epileptic Seizure Classification of EEG signals using XGBoost Classifier

2020 ◽  
Vol 14 (5) ◽  
Keyword(s):  
Epileptic Seizure ◽  
Eeg Signals ◽  
Seizure Classification

scholarly journals Classification and Severity Measurement of Epileptic Seizure using Intracranial Electroencephalogram (iEEG)

2020 ◽  
Vol 10 (2) ◽  
pp. 36-41
Author(s):  
Sanjay Shamrao Pawar ◽  
Sangeeta Rajendra Chougule
Keyword(s):  
Epileptic Seizure ◽  
Life Quality ◽  
Focal Seizure ◽  
Measurement Scale ◽  
Medical Test ◽  
Seizure Severity ◽  
Intracranial Electroencephalography ◽  
Seizure Classification

The Epileptic seizure is one of major neurological brain disorders and about 50 million of world’s population is affected by it. Electroencephalography is medical test which records brain signal by mounting electrodes on scalp or brain cortex to diagnosis seizure. Scalp Electroencephalography has low spatial resolution and presence of external artifact as compared to Intracranial Electroencephalography. In Intracranial Electroencephalography strip, grid and depth type of electrodes are implanted on cortex of brain by surgery to measure brain signal. Analysis of brain signal was carried out in past in diagnosis of Epileptic seizure. Seizure classification and Severity measurement of Epileptic Seizure are still challenging areas of research. Seizures are classified as focal seizure, generalized and secondary generalized seizure depending upon the area of brain which it generates and how it spreads. Classification of seizure helps in treatment of seizure and during brain surgery to operate on brain part which is responsible for continuous seizures generation. Developed seizure classification algorithm classifies seizures as focal Seizure, generalized Seizure and secondary generalized seizure depending on the percentage of iEEG electrodes detecting seizure activity. Seizure severity measurement scale is developed by modification in National Hospital Seizure Severity Scale. Seizures are graded as Mild seizure, Moderate seizure and severe seizure depending on its severity. Seizure Classification and Seizure Severity Measurement improves life quality of Epileptic patients by proper drug management.

Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG

2017 ◽  
Vol 27 (04) ◽  
pp. 1750005 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Qing Cai ◽  
Yu-Xuan Yang ◽  
Na Dong ◽  
Shan-Shan Zhang
Keyword(s):  
Epileptic Seizure ◽  
Healthy Subjects ◽  
Visibility Graph ◽  
Clustering Coefficient ◽  
Eeg Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Brain States ◽  
Optimal Kernel

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

scholarly journals Epileptic Seizures Detection based on Empirical Mode Decomposition and Hilbert-Huang transform of EEG Signal

2015 ◽  
Vol 1 (2) ◽  
pp. 295
Author(s):  
Mokhtar Mohammadi ◽  
Aso M. Darwesh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Spectral Energy ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

MULTIFRACTAL-WAVELET BASED DENOISING IN THE CLASSIFICATION OF HEALTHY AND EPILEPTIC EEG SIGNALS

2012 ◽  
Vol 11 (04) ◽  
pp. 1250034 ◽  
Author(s):  
R. UTHAYAKUMAR ◽  
D. EASWARAMOORTHY
Keyword(s):  
Epileptic Seizure ◽  
Fractal Dimensions ◽  
Biomedical Sciences ◽  
Eeg Signals ◽  
Denoising Method ◽  
Identification Rate ◽  
Processing Step ◽  
Good Improvement ◽  
Multifractal Measures

Identification of abnormality in Electroencephalogram (EEG) signals is the vast area of research in the neuroscience. Especially, the classification of healthy and epileptic subjects through EEG signals is the crucial problem in the biomedical sciences. Denoising of EEG signals is another important task in signal processing. The noises must be corrected or reduced before the subsequent decision analysis. This paper presents a wavelet-based denoising method for the recovery of EEG signal contaminated by nonstationary noises and investigates the recognition of healthy and epileptic EEG signals by using multifractal measures such as Generalized Fractal Dimensions. The multifractal measures show the significant differences among normal, interictal and epileptic ictal EEGs with denoising by wavelet transform as the pre-processing step. The denoised artifact-free EEG presents a very good improvement in the identification rate of epileptic seizure. The proposed scheme illustrates with high accuracy through the suitable graphical and statistical tools and performs an important role in the epileptic seizure detection.

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