Multichannel detection of epilepsy using SVM classifier on EEG signal

2016 ◽  
Author(s):  
Manish N. Tibdewal ◽  
Swapnil A. Tale
Keyword(s):  
Svm Classifier ◽  
Eeg Signal ◽  
Multichannel Detection

scholarly journals Seizure Detection using SVM Classifier on EEG Signal

2014 ◽  
Vol 14 (14) ◽  
pp. 1658-1661 ◽  
Author(s):  
B. Suguna Nanthini ◽  
B. Santhi
Keyword(s):  
Seizure Detection ◽  
Svm Classifier ◽  
Eeg Signal

scholarly journals An EMD and IMF Energy Entropy Based Optimized Feature Extraction and Classification Scheme for Single Trial EEG Signal

2021 ◽  
Author(s):  
Song Luo ◽  
PeiYun Zhong ◽  
Rui Chen ◽  
CunYang Pan ◽  
KeYu Liu ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Classification Accuracy ◽  
Gaussian Mixture ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Single Trial ◽  
Energy Entropy

Abstract For the purpose of improving the classification accuracy of single trial EEG signal during motor imagery (MI) process, this study proposed a classification method which combined IMF energy entropy and improved EMD scheme. Singular value decomposition (SVD), Gaussian mixture model, EMD and IMF energy entropy were employed for the newly designed scheme. After removing noise and artifacts from acquired EEG signals in EEGLAB, SVD was applied, and the singular values were clustered by Gaussian mixture model. The insignificant characteristics indicated by the small SVD values were then removed, and the signals were reconstructed, feeding to EMD algorithm. Those IMFs mapping to δ、θ、α and β frequencies were selected as the major features of the EEG signal. The SVM classifier with RBF, linear, and polynomial kernel functions and voting mechanism then kicked in for classification. The results were compared with that of the traditional EMD and EEMD through simulation, showing that the proposed scheme can eliminate mode mixing effectively and improve the single trial EEG signal classification accuracy significantly, suggesting the probability of designing a more efficient EEG control system based on the proposed scheme.

scholarly journals EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

2019 ◽  
Vol 8 (6S4) ◽  
pp. 683-688
Keyword(s):  
Feature Extraction ◽  
Cell Activity ◽  
Vital Role ◽  
Brain Cell ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Machine Learning Classification ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
The Brain

Epilepsy is censorious neurological disorder in which nerve cell activity in the brain is disturbed causing recurrent seizures which are sudden, uncontrolled electrical discharges in the brain cell. In clinical treatment of epileptic patients seizure reorganization has much prominence. Hence in detecting the phenomenon of epilepsy Electroencephalogram (EEG) signal is widely used as it includes important carnal data of the brain. Though it is critical to analyze the EEG signal and identify the seizures. So feature extraction of EEG signal plays a vital role for epilepsy detection. This paper describes an worthwhile feature extraction based on variational mode decomposition (VMD) to identify epilepsy. The extracted features fed to ANN, KNN and SVM in order to classify epilepsy. The performance of the SVM classifier shows the better classification compared to existing methods.

scholarly journals Epileptic EEG signal classifications based on DT-CWT and SVM classifier

2021 ◽  
Author(s):  
Deivasigamani S ◽  
◽  
Senthilpari C ◽  
Wong Hin Yong ◽  
Rajesh P.K. ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
The Other ◽  
Support Vector ◽  
Svm Classifier ◽  
Medical Procedure ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Sensitivity Specificity ◽  
The Mind

Contamination in human cerebrum causes the mind issue which is as Epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs and the other sound locales in the mind produce the standard example signals as non-focal sign. Henceforth, the discovery of focal signs from the non-focal signs is a significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for EEG (Electroencephalogram) signals orders utilizing SVM (Support Vector Machine) classifier. The exhibition of the proposed EEG signals characterization framework is assessed as far as Sensitivity, Specificity, and Accuracy.

A NOVEL APPROACH FOR EPILEPSY DETECTION USING TIME–FREQUENCY LOCALIZED BI-ORTHOGONAL WAVELET FILTER

2019 ◽  
Vol 19 (01) ◽  
pp. 1940007 ◽  
Author(s):  
MANISH SHARMA ◽  
SUNNY SHAH ◽  
P. V. ACHUTH
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Classification Problems ◽  
Orthogonal Wavelet ◽  
Wavelet Filters ◽  
Time Frequency ◽  
Novel Approach ◽  
Public Dataset ◽  
Electroencephalogram Eeg

Identification of seizure using electroencephalogram (EEG) signal is crucial to detect diseases like epilepsy. Manual epilepsy detection is time consuming and prone to errors. Various techniques have been developed to get quick and accurate results for epilepsy detection. We propose a novel approach to detect epileptic seizure using bi-orthogonal wavelet filters. The bi-orthogonal wavelet filters divide the EEG signal into different sub-bands. Then different features, i.e., Shannon entropy (ShEn), Renyi entropy (RenEn), fractal dimension (FD), energy and fuzzy entropy (FE) are extracted from the sub-bands. The [Formula: see text]-values of the features are used to evaluate the discriminating ability of the features. These features are given as input to the support vector machine (SVM). The EEG signals are then classified into the following classes: (i) normal versus seizure, (ii) seizure-free versus seizure, (iii) normal versus seizure-free versus seizure, and (iv) normal versus seizure-free. We have used two independent datasets: a public dataset for training and validation of the model, and a private dataset for evaluating the model’s performance. The ten-fold cross-validation method is used here to reduce the chances of over fitting. The SVM classifier yielded 100% accuracy in discriminating both seizure-free and normal patients for public dataset, and inter-ictal and ictal for private dataset. It also gave very good classification accuracies for the other classification problems for both the datasets. The proposed method is ready for the clinical trial to be tested with huge databases before the actual practical usage.

scholarly journals Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Mostafizur Rahman ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Correlation Coefficient ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Statistical Features ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
High Level ◽  
Mental Tasks

In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.

scholarly journals Perbandingan Metode Klasifikasi untuk Menentukan Tingkat Kenyamanan Suhu pada Kondisi Rileks Berbasis Sinyal EEG

2019 ◽  
Vol 10 (2) ◽  
pp. 93-97
Author(s):  
Laurentius Kuncoro Probo Saputra ◽  
Ignatia Dhian Estu Karisma Ratri
Keyword(s):  
Standard Deviation ◽  
Thermal Comfort ◽  
Brain Activity ◽  
Control Mode ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Air Conditioner ◽  
Band Frequency ◽  
Knn Classifier ◽  
Target Environment

Temperature control on air conditioner devices is still oriented to the target environment. This control mode ignores one's physiological condition. A person's thermal comfort varies when indoors. Thermal comfort is closely related to environmental thermal satisfaction conditions. EEG signal is a signal that can reflect brain activity. This research objective is provide classifier model for classifiying person’s thermal comfort based on eeg signal. This research used three conditions of room’s temperature. The features used by classfier are avarage frequency band, HFD, PFD, and MSE features. Classifier performance was assessed using ROC curve evaluation. The results of the classification of thermal comfort levels with EEG signals with the KNN classifier are obtained only by using the band frequency average feature, which is equal to 0.878 with a standard deviation of 0.022. While the SVM classifier gets the highest performance by using a combination of the average band + HFD frequency feature, which is 0.877 with a standard deviation of 0.013 in the linear kernel and RBF.

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