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 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 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.

Traditionally, biological signals are generated as one-dimensional arrays (even if acquired with many channels) and consequently encoded through one-dimensional techniques. Nonetheless, some researchers have addressed the encoding of biological records as two-dimensional arrays, in such a way that signal dependencies are exploited by two-dimensional encoders (e.g., video and image encoders), which are preceded by adaptation steps. The main goal of the latter is to reshape input signals and make their structures more suitable to target encoders, in order to favor dependency exploration and then provide higher performance. The present work employs a similar approach for electroencephalograms, but with the use of a new preprocessing technique, named as percentage difference segmentation, which is combined with the H.264 and high efficiency video coding compressors. Simulation results show that the proposed methodology is effective and outperforms state-of-the-art schemes present in the literature, in terms of P RD × compression ratio.

Abstract-Recently, two-dimensional techniques were successfully employed for encoding surface electromyographic (S-EMG) records, through the use of off-the-shelf image encoders as an effective alternative for that kind of signal. However, as S-EMG signals are very different from natural images, there is often a preprocessing step before compression, in an attempt to improve the performance of the chosen encoder. This paper address the mentioned approach and presents an investigation regarding the performance of video and image encoders, when used for compressing S-EMG signals. In addition, two new preprocessing techniques are introduced, named as euclidean distance sorting (EDS) and region-based euclidean distance sorting (REDS), which have the potential to enhance the exploitation of intersegment correlations, normally present on S-EMG records. Experiments were carried out with real isometric records acquired in laboratory, which were firstly preprocessed and then compressed with the JPEG2000, H.264/advanced video coding, and high efficiency video coding (HEVC) algorithms. A brief analysis reveals that the proposed scheme is effective, given that JPEG2000 and HEVC allied to EDS and REDS even outperform state-of-the-art schemes available in the literature, in terms of PRD × Compression Ratio and spectral-parameter estimation.