Evidence map›Paper›PMID 27091454›Full record

ArticleBiomedical engineering online2016

SEMG signal compression based on two-dimensional techniques.

Wheidima Carneiro de Melo, Eddie Batista de Lima Filho, Waldir Sabino da Silva Júnior

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Article in Biomedical engineering online, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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3 authors.

Wheidima Carneiro de MeloState University of Amazonas, Av. Darcy Vargas, 1200, Parque 10, 69050-020, Manaus, Brazil. wmelo@uea.edu.br.
Eddie Batista de Lima FilhoFederal University of Amazonas, Av. General Rodrigo Octávio, 6200, Coroado I, 69077-000, Manaus, Brazil.
Waldir Sabino da Silva JúniorFederal University of Amazonas, Av. General Rodrigo Octávio, 6200, Coroado I, 69077-000, Manaus, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders. Such schemes usually provide specific compressors, which are tuned for SEMG data, or employ preprocessing techniques, before the two-dimensional encoding procedure, in order to provide a suitable data organization, whose correlations can be better exploited by off-the-shelf encoders. Besides preprocessing input matrices, one may also depart from those approaches and employ an adaptive framework, which is able to directly tackle SEMG signals reassembled as images.

methodsThis paper proposes a new two-dimensional approach for SEMG signal compression, which is based on a recurrent pattern matching algorithm called multidimensional multiscale parser (MMP). The mentioned encoder was modified, in order to efficiently work with SEMG signals and exploit their inherent redundancies. Moreover, a new preprocessing technique, named as segmentation by similarity (SbS), which has the potential to enhance the exploitation of intra- and intersegment correlations, is introduced, the percentage difference sorting (PDS) algorithm is employed, with different image compressors, and results with the high efficiency video coding (HEVC), H.264/AVC, and JPEG2000 encoders are presented.

resultsExperiments were carried out with real isometric and dynamic records, acquired in laboratory. Dynamic signals compressed with H.264/AVC and HEVC, when combined with preprocessing techniques, resulted in good percent root-mean-square difference [Formula: see text] compression factor figures, for low and high compression factors, respectively. Besides, regarding isometric signals, the modified two-dimensional MMP algorithm outperformed state-of-the-art schemes, for low compression factors, the combination between SbS and HEVC proved to be competitive, for high compression factors, and JPEG2000, combined with PDS, provided good performance allied to low computational complexity, all in terms of percent root-mean-square difference [Formula: see text] compression factor.

conclusionThe proposed schemes are effective and, specifically, the modified MMP algorithm can be considered as an interesting alternative for isometric signals, regarding traditional SEMG encoders. Besides, the approach based on off-the-shelf image encoders has the potential of fast implementation and dissemination, given that many embedded systems may already have such encoders available, in the underlying hardware/software architecture.

Indexed as

ElectromyographySignal Processing, Computer-AssistedAlgorithmsData CompressionHEVCMultidimensional multiscale parserPreprocessing techniqueSEMG

Identifiers

PMID27091454
PMCPMC4835940

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.