Electromyographic signal compression based on preprocessing techniques.

Recently, electromyographic records have been rearranged into two-dimensional arrays and encoded with image compressors, in the same way as image data. However, as a consequence of this reshaping, the correlation among signal segments is generally lost, which reduces the compression efficiency. In the present work, new preprocessing techniques for encoding electromyographic signals as two-dimensional matrices are presented, namely percentage difference sorting and relative complexity sorting, which have the potential to favor the exploitation of the intersegment dependencies. The experiments were carried out with real isometric records acquired in laboratory, that were first preprocessed and then compressed with a JPEG2000 encoder, showing that the proposed framework is effective and outperforms even state-of-the-art schemes present in the literature, in terms of PRD × Compression Ratio.

On ECG signal compression with 1-D multiscale recurrent patterns allied to preprocessing techniques.

This paper presents the results of a multiscale pattern-matching-based ECG encoder, which employs simple preprocessing techniques for adapting the input signal. Experiments carried out with records from the Massachusetts Institute of Technology-Beth Israel Hospital database show that the proposed scheme is effective, outperforming some state-of-the-art schemes described in the literature.

On EMG signal compression with recurrent patterns.

In this paper, the multidimensional multiscale parser (MMP) is employed for encoding electromyographic signals. The experiments were carried out with real signals acquired in laboratory and show that the proposed scheme is effective, outperforming even wavelet-based state-of-the-art schemes present in the literature in terms of percent root mean square difference x compression ratio.

ECG signal compression based on Dc equalization and complexity sorting.

In this brief, we present new preprocessing techniques for electrocardiogram signals, namely, dc equalization and complexity sorting, which when applied can improve current 2-D compression algorithms. The experimental results with signals from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database outperform the ones from many state-of-the-art schemes described in the literature.