International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Denoising of EEG Signals For Analysis of Brain Disorders: A Review Mohammad Ziaullah1, Zuber Ahmed Punekar2, Ujwala S3 , M. Megha4 Bhagyashri M5 1,2 Asst
Professor, Dept of ECE, SECAB I.E.T, Vijayapur, Karnataka, India B.E Graduate, Dept of ECE, SECAB I.E.T, Vijayapur, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------3,4,5
Abstract - The Electroencephalogram (EEG) signal is a biological non-stationary signal which contains important information about various activities of Brain. Analysis of EEG signals is useful for diagnosis of many neurological diseases such as epilepsy, tumors, and various problems associated with trauma. EEG measured by placing electrodes on scalp usually has very small amplitude, so the analysis of EEG signal and the extraction of information from this signal is a difficult problem. EEG signals usually were contaminated with unwanted artifacts that may hide some valuable information in the signals. EEG signal become more complicated to analyze by the introduction of artifacts such as line noise, eye blinks, eye movements, heartbeat, breathing, and other muscle activities. Proper diagnosis of disease requires faultless analysis of the EEG signals. The problem of denoising is quite varied due to variety of signals and noise. Therefore this paper presents a review on denoising of EEG signals and concludes that discrete wavelet transform provides effective solution for denoising nonstationary signals such as EEG.
Feature extraction is a process whereby the relevant information or characteristics from the signal is extracted so that the features can be easily interpreted. Therefore, it is a substantial process in interpreting an input signal. The information extract reflects the physiology and anatomy of the activity going on within the brain. It involved a number of variables in a large set of data, which require a large amount of memory or powerful algorithm to analyze the data. In this context, feature extraction method is needed in order to resolve these variables or information to be interpreted in a simple and accurate way. Some of the techniques that can be used for the noise removal are ICA denoising [2] PCA method of denoising [4], Wavelet based denoising [5] , Wavelet packet based denoising and so on. All the above methods can be implemented for the denoising of the EEG signals and their performance evaluation can be done by measuring the parameters like SNR, PSNR, and MSE etc . The aim of the paper is to review the de-noising signal processing techniques for the removal of artifacts in EEG signals.
Key Words: epilepsy,tumors, artifacts, diagnosis,EEG. 1.INTRODUCTION Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population (Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and transient disturbances of perception or behaviour resulting from excessive synchronization of cortical neuronal networks; it is a neurological condition in which an individual experiences chronic abnormal bursts of electrical discharges in the brain Monitoring brain activity through the electroencephalogram (EEG) has become an important tool in the diagnosis of epilepsy. Epileptic people are two or three times more likely to die prematurely when compared to a normal person. Hence, study of epilepsy has always been an utmost importance in the biomedical field of research. Epilepsy is a chronic brain disorder, characterized by seizures, which can affect any person at any age. The epileptic seizures occur because of the malfunctioning of the electrophysiological system of the brain, which causes sudden excessive electrical discharge in a group of brain cells (i.e. neurons)present in the cerebral cortex. Involvement of cerebral cortex leadsto abnormalities of motor functions causing jerky (tonic-clonic) spasms of muscles and joints. Š 2017, IRJET
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Section II describes about Brain waves and EEG signals. Section III describes literature survey. Section IV describes the various de-noising techniques .Section V presents the conclusion. I.
BRAIN WAVE AND EEG SIGNALS
The analysis of brain wave plays an vital role in diagnosis of different brain disorders. Brain produces electrical signals which can be detected using EEG which is measured by electrodes placed over the scalp. This technique is noninvasive as no surgery is required. The one of the biggest challenge in using EEG is the very small signal-to-noise ratio of brain signals which is contaminated by various noise sources or artifacts.
Figure 1: Various bands of EEG signal ISO 9001:2008 Certified Journal
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