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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Vice Chancellery for Research and Technology, University of Tabriz</PublisherName>
				<JournalTitle>Advanced Signal Processing</JournalTitle>
				<Issn>2676-3397</Issn>
				<Volume>3</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2019</Year>
					<Month>05</Month>
					<Day>22</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Electromyogram Signal Compression Based on Empirical-Mode-Decomposition-Based Approximation and DCT-Based Smoothing</ArticleTitle>
<VernacularTitle>Electromyogram Signal Compression Based on Empirical-Mode-Decomposition-Based Approximation and DCT-Based Smoothing</VernacularTitle>
			<FirstPage>83</FirstPage>
			<LastPage>96</LastPage>
			<ELocationID EIdType="pii">9184</ELocationID>
			
<ELocationID EIdType="doi">10.22034/jasp.2019.9184</ELocationID>
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Magari</LastName>
<Affiliation>Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran</Affiliation>

</Author>
<Author>
					<FirstName>H.</FirstName>
					<LastName>Grailu</LastName>
<Affiliation>Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2017</Year>
					<Month>11</Month>
					<Day>16</Day>
				</PubDate>
			</History>
		<Abstract>Electromyogram (EMG) signals are useful in muscle behavior assessment and have some clinical applications. Today, there is a great tendency to transmit and store long-term EMG recordings which implies the importance of EMG signal compression. In this paper, we have proposed an EMG signal compression approach based on Empirical-Mode-Decomposition-based signal approximation, Discrete-Cosine-Transform-based signal smoothing, two-dimensional signal processing, wavelet transform, and SPIHT coding. We have evaluated the compression performance of the proposed approach by two sets of measures: The compression throughput and clinical-information-preserving measures. The former include two measures of PRD and CF while the latter uses four spectral parameters as the appropriate measures.</Abstract>
			<OtherAbstract Language="FA">Electromyogram (EMG) signals are useful in muscle behavior assessment and have some clinical applications. Today, there is a great tendency to transmit and store long-term EMG recordings which implies the importance of EMG signal compression. In this paper, we have proposed an EMG signal compression approach based on Empirical-Mode-Decomposition-based signal approximation, Discrete-Cosine-Transform-based signal smoothing, two-dimensional signal processing, wavelet transform, and SPIHT coding. We have evaluated the compression performance of the proposed approach by two sets of measures: The compression throughput and clinical-information-preserving measures. The former include two measures of PRD and CF while the latter uses four spectral parameters as the appropriate measures.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Compression</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">empirical mode decomposition (EMD)</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">signal smoothing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">discrete cosine transform (DCT)</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">two-dimensional signal processing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">wavelet transform</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">set partitioning in hierarchical trees (SPIHT) coding</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jasp.tabrizu.ac.ir/article_9184_12c35c5392959177781ba38936ae7450.pdf</ArchiveCopySource>
</Article>
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