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<journal-meta>
<journal-id journal-id-type="publisher">global-journal-of-medical-research-d-radiology-diagnostic</journal-id>
<journal-title-group>
<journal-title>Global Journal of Medical Research - D: Radiology, Diagnostic</journal-title>
</journal-title-group>
<issn publication-format="print">0975-5888</issn>
<issn publication-format="electronic">2249-4618</issn>
<publisher><publisher-name>Global Journals Publishing Group Incorporated</publisher-name></publisher>
<self-uri xlink:href="https://globaljournals.org/journal-seo-export/jats/60199.xml" />
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<article-id pub-id-type="publisher-id">60199</article-id>
<title-group>
<article-title>Denoising and Analysis of EMG Signal using Wavelet Transform</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Ara</surname><given-names>Iffat</given-names></name><xref ref-type="aff" rid="aff1" />
</contrib>
</contrib-group>
<aff id="aff1">BANGLADESH, Pabna University of Science and Technology</aff>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2020-01-15">
<day>15</day>
<month>01</month>
<year>2020</year>
</pub-date>
<volume>20</volume>
<issue>D1</issue>
<fpage>13</fpage>
<lpage>19</lpage>
<abstract><p>EMG is the recording of the electrical activity produced within the muscle fibers. Measurement of EMG signal is corrupted by additive noise whose signal-to-noise ratio (SNR) varies. Feature extraction is an important step for EMG classification. Time domain and frequency domain parameters were chosen as representative features for EMG signals. In this thesis, the Wavelet transform and wavelet coefficients have adopted to represent the EMG signals. Wavelet transform (WT) has been applied also in this research for the analysis of the surface electromyography signal (SEMG). The properties of wavelet transform turned out to be suitable for nonstationary EMG signals. Also Spectrum analysis has been applied to various types of EMG signal.</p></abstract>
<kwd-group kwd-group-type="author-generated">
<kwd>EMG</kwd>
<kwd>wavelet transform</kwd>
<kwd>SNR</kwd>
<kwd>myopathy</kwd>
<kwd>neuropathy.</kwd>
</kwd-group>
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<title>Full Text</title>
<p>EMG is the recording of the electrical activity produced within the muscle fibers. Measurement of EMG signal is corrupted by additive noise whose signal-to-noise ratio (SNR) varies. Feature extraction is an important step for EMG classification. Time domain and frequency domain parameters were chosen as representative features for EMG signals. In this thesis, the Wavelet transform and wavelet coefficients have adopted to represent the EMG signals. Wavelet transform (WT) has been applied also in this research for the analysis of the surface electromyography signal (SEMG). The properties of wavelet transform turned out to be suitable for nonstationary EMG signals. Also Spectrum analysis has been applied to various types of EMG signal.</p>
</sec>
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