Study of EEG based on SVM and SVM with EMD
نویسندگان
چکیده
Abstract Study electroencephalograph (EEG) of epileptic patients during different periods in order to do effective treatments. This paper studies wave forms and energy characteristics of the paroxysmal stage and the static epileptic EEG, adopts two different methods to classify. One is using Support Vector Machines (SVM) to rebuild and classify as to the characteristic signals. The other is applying Empirical Mode Decomposition (EMD) and SVM together to classify EEG. After experimenting and testing the epileptic data, the general discrimination of EEG is 94.5% and 96.05% respectively. The result indicates that using SVM and EMD can achieve more ideal effect, classify the epileptic EEG and normal EEG.
منابع مشابه
EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach
Introduction: Clinicians use several computer-aided diagnostic systems for depression to authorize their diagnosis. An electroencephalogram (EEG) may be used as an objective tool for early diagnosis of depression and controlling it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a no...
متن کاملEpileptic Seizure Detection in EEG signals Using TQWT and SVM-GOA Classifier
Background: Epilepsy is a Brain disorder disease that affects people's quality of life. If it is diagnosed at an early stage, it will not be spread. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. However, this screening system cannot diagnose epileptic seizure states precisely. Nevertheless, with the help of computer-aided diagnosis systems (CADS), neurologists ca...
متن کاملRecognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier
This paper presents a new framework based on modified EMD method for detection of single and multiple PQ issues. In modified EMD, DWT precedes traditional EMD process. This scheme makes EMD better by eliminating the mode mixing problem. This is a two step algorithm; in the first step, input PQ signal is decomposed in low and high frequency components using DWT. In the second stage, the low freq...
متن کاملMental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...
متن کاملClassification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in EEG Signal
The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...
متن کامل