مقاله انگلیسی رایگان در مورد طبقه بندی سیگنال EEG با استفاده از ماشین بردار پشتیبانی جهان – الزویر 2018

Support vector machine (SVM) has been used widely for classification of electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as epilepsy and sleep disorders. SVM shows good generalization performance for high dimensional data due to its convex optimization problem. The incorporation of prior knowledge about the data leads to a better optimized classifier. Different types of EEG signals provide information about the distribution of EEG data. To include prior information in the classification of EEG signals, we propose a novel machine learning approach based on universum support vector machine (USVM) for classification. In our approach, the universum data points are generated by selecting universum from the EEG dataset itself which are the interictal EEG signals. This removes the effect of outliers on the generation of universum data. Further, to reduce the computation time, we use our approach of universum selection with universum twin support vector machine (UTSVM) which has less computational cost in comparison to traditional SVM. For checking the validity of our proposed methods, we use various feature extraction techniques for different datasets consisting of healthy and seizure signals. Several numerical experiments are performed on the generated datasets and the results of our proposed approach are compared with other baseline methods. Our proposed USVM and proposed UTSVM show better generalization performance compared to SVM, USVM, Twin SVM (TWSVM) and UTSVM. The proposed UTSVM has achieved highest classification accuracy of 99 % for the healthy and seizure EEG signals.

Introduction

Electroencephalogram (EEG) signal classification is a major challenge in the field of machine learning and signal processing. EEG is widely used non-invasive technique for the detection of various types of brain disorders such as epileptic seizures and sleep disorders. In epilepsy, the extent of disease ranges from partial to generalized seizures which are reflected in their respective EEG. The different types of EEG signals are shown in fig. 2. For the better feature extraction and classification of EEG signals, several signal processing techniques have been used by researchers. Among the various feature extraction techniques, wavelet transform is one of the frequently used methods. In wavelet transform, the frequency domain features are extracted from the signal with good localization in time which is in contrast to the Fourier transform where the signal analysis is done mainly in the frequency domain. In wavelet analysis, the approximation and decomposition coefficients are used to form the feature vector as shown in fig. 3. The different families of wavelet are used for specific type of signals to get better characteristics of that signal. Adeli et al. (2003) proposed a computer aided diagnosis (CAD) method for epilepsy using discrete wavelet transform (DWT). They used Daubechies wavelet with db-4 as the mother wavelet for the feature extraction. Rosso et al. (2005) used orthogonal decimated discrete wavelet transform (ODWT) for detecting maturational changes associated with childhood absence epilepsy. Ocak (2008) performed the classification of EEG signals using wavelet packet analysis and genetic algorithm. Daubechies wavelet-2 is used for the classification of five different EEG signals (Guler & Ubeyli, 2005). Subasi and Gursoy (2010) used principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) for the feature extraction, and support vector machine (SVM) for classification. The proper selection of classification techniques is very crucial for the automated diagnosis of patients having neurological diseases. Among the various classification algorithms, support vector machines (SVMs) (Cortes and Vapnik, 1995) have emerged as a powerful classification technique. SVM solves a convex optimization problem which leads to a globally optimal solution. This is in contrast to artificial neural network (ANN) that suffers from the problem of local minima.