Limits...
SEMG signal compression based on two-dimensional techniques.

de Melo WC, de Lima Filho EB, da Silva Júnior WS - Biomed Eng Online (2016)

Bottom Line: Recently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders.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, 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.

View Article: PubMed Central - PubMed

Affiliation: State University of Amazonas, Av. Darcy Vargas, 1200, Parque 10, 69050-020, Manaus, Brazil. wmelo@uea.edu.br.

ABSTRACT

Background: Recently, 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.

Methods: This 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.

Results: Experiments 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.

Conclusion: The 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.

No MeSH data available.


Compression result for a segment from one of the test isometric SEMG signals, at a CF of 87.3 %: a signal reconstruction with SbS + HEVC, and b error signal, c signal reconstruction with PDS + HEVC and d error signal, e signal reconstruction with SbS + H.264/AVC and f error signal, g signal reconstruction with PDS + H.264/AVC, and h error signal, i signal reconstruction with SbS + JPEG2000, and j error signal, and k signal reconstruction with PDS + JPEG2000, and l error signal
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4835940&req=5

Fig13: Compression result for a segment from one of the test isometric SEMG signals, at a CF of 87.3 %: a signal reconstruction with SbS + HEVC, and b error signal, c signal reconstruction with PDS + HEVC and d error signal, e signal reconstruction with SbS + H.264/AVC and f error signal, g signal reconstruction with PDS + H.264/AVC, and h error signal, i signal reconstruction with SbS + JPEG2000, and j error signal, and k signal reconstruction with PDS + JPEG2000, and l error signal

Mentions: The same conclusions presented in the last paragraph can be used for Fig. 13, whose reconstructed and error signals are related to the proposed image encoders and preprocessing techniques, with the same input signal and CF. Moreover, as the performance of each association between preprocessing technique and encoder is different, the resulting error signals are also distinct, although they present a scale that is ten times smaller.Fig. 13


SEMG signal compression based on two-dimensional techniques.

de Melo WC, de Lima Filho EB, da Silva Júnior WS - Biomed Eng Online (2016)

Compression result for a segment from one of the test isometric SEMG signals, at a CF of 87.3 %: a signal reconstruction with SbS + HEVC, and b error signal, c signal reconstruction with PDS + HEVC and d error signal, e signal reconstruction with SbS + H.264/AVC and f error signal, g signal reconstruction with PDS + H.264/AVC, and h error signal, i signal reconstruction with SbS + JPEG2000, and j error signal, and k signal reconstruction with PDS + JPEG2000, and l error signal
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4835940&req=5

Fig13: Compression result for a segment from one of the test isometric SEMG signals, at a CF of 87.3 %: a signal reconstruction with SbS + HEVC, and b error signal, c signal reconstruction with PDS + HEVC and d error signal, e signal reconstruction with SbS + H.264/AVC and f error signal, g signal reconstruction with PDS + H.264/AVC, and h error signal, i signal reconstruction with SbS + JPEG2000, and j error signal, and k signal reconstruction with PDS + JPEG2000, and l error signal
Mentions: The same conclusions presented in the last paragraph can be used for Fig. 13, whose reconstructed and error signals are related to the proposed image encoders and preprocessing techniques, with the same input signal and CF. Moreover, as the performance of each association between preprocessing technique and encoder is different, the resulting error signals are also distinct, although they present a scale that is ten times smaller.Fig. 13

Bottom Line: Recently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders.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, 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.

View Article: PubMed Central - PubMed

Affiliation: State University of Amazonas, Av. Darcy Vargas, 1200, Parque 10, 69050-020, Manaus, Brazil. wmelo@uea.edu.br.

ABSTRACT

Background: Recently, 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.

Methods: This 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.

Results: Experiments 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.

Conclusion: The 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.

No MeSH data available.