scholarly journals Performance of Support Vector Machine in Classifying EEG Signal of Dyslexic Children using RBF Kernel

2018 ◽  
Vol 9 (2) ◽  
pp. 403
Author(s):  
AZA Zainuddin ◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
Z. Mahmoodin
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Reading And Writing ◽  
Theta Waves ◽  
Rbf Kernel ◽  
Electroencephalogram Eeg ◽  
Kernel Parameters

Dyslexia is referred as learning disability that causes learner having difficulties in decoding, reading and writing words. This disability associates with learning processing region in the human brain. Activities in this region can be examined using electroencephalogram (EEG) which record electrical activity during learning process. This study looks into performance of Support Vector Machine (SVM) using RBF kernel in classifying EEG signal of Normal, Poor and Capable Dyslexic children during writing words and non-words. Discrete Wavelet Transform (DWT) with Daubechies order 2 was employed to extract the power of beta and theta waves of EEG signal. Beta and Theta/Beta ratio form the input features for classifier.  Multiclass one versus one SVM was used in the classification where RBF kernel parameters and box constraint values were varied with the factor of 10 to analyze performance of the classifier. It was found that the best performance of SVM with 91% overall accuracy was obtained when both kernel scale and box constraint are set to one.

scholarly journals EEG signal classification for drowsiness detection using wavelet transform and support vector machine

2021 ◽  
Vol 10 (2) ◽  
pp. 501
Author(s):  
Novie Theresia Br. Pasaribu ◽  
Timotius Halim ◽  
Ratnadewi Ratnadewi ◽  
Agus Prijono
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Electroencephalogram Eeg ◽  
Behavioral Methods ◽  
Physiological Method ◽  
Fold Cross Validation

<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>

scholarly journals Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

2018 ◽  
Vol 7 (4.10) ◽  
pp. 935
Author(s):  
Vasudha Harlalka ◽  
Viraj Pradip Puntambekar ◽  
Kalugotla Raviteja ◽  
P. Mahalakshmi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Shannon Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Energy Entropy ◽  
Electroencephalogram Eeg ◽  
Logarithmic Energy

Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine     (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.  

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

EEG Signals Classification Using Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 215-224
Author(s):  
Abdelhakim Ridouh ◽  
Daoud Boutana ◽  
Salah Bourennane
Keyword(s):  
Support Vector Machine ◽  
Wavelet Decomposition ◽  
Moving Average ◽  
Real Life ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Decomposition Level ◽  
Sensitivity Specificity

We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.

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