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  • A Study on Discrete Wavelet-based Noise Removal from EEG Signals

    Author
    Asaduzzaman, K.
    Reaz, M.
    Mohd-Yasin, F.
    Sim, K.
    Hussain, M.
    Year published
    2010
    Metadata
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    Abstract
    Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
    Book Title
    Advances in Computational Biology
    DOI
    https://doi.org/10.1007/978-1-4419-5913-3_65
    Subject
    Electrical and Electronic Engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/40483
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