Relative Wavelet Energy as a New Feature Extractor for Sleep Classification Using Eeg Signals
نویسندگان
چکیده
In Electroencephalography (EEG) processing one of the crucial step is to select the features which can be used to characterize the different patterns. This paper presents relative wavelet energy as a new feature extraction technique to recognize the sleep EEG patterns. Experimental results on the Physionet databases with different sleep stages indicate that the relative wavelet energy is a computationally efficient and accurate method and can be used for successfully for EEG feature extraction.
منابع مشابه
Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers
Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...
متن کاملApplying Genetic Algorithm to EEG Signals for Feature Reduction in Mental Task Classification
Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states. Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...
متن کاملFeature Extraction of Visual Evoked Potentials Using Wavelet Transform and Singular Value Decomposition
Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...
متن کاملFault diagnosis of gearboxes using LSSVM and WPT
This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...
متن کاملComputer Assisted Automatic Sleep Scoring System Using Relative Wavelet Energy Based Neuro Fuzzy Model
This paper addresses the automated scoring of sleep stages using Electroencephalograph (EEG). The change in the Sleep Stages is accompanied by changes in the frequency spectrum of the EEG signals. A novel method based on Relative Wavelet Energy based Neuro-fuzzy is proposed to perform automatic sleep stages classification. Features extracted from 30-second epoch of (EEG) using relative wavelet ...
متن کامل