Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

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.