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The efficacy of four powerline interference suppression methods was tested for application in highresolution electrocardiogram (ECG)

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His-bundle electrocardiogram micropotentials are usually obtained by serial-signal averaging, because no acceptable, satisfactory solution for beat-by-beat removal of power-line interference and electromyographic noise has been found. A method has been developed for surface beat-to-beat His-bundle potential recovery, with the hypothesis that no distortion in the signal shape is admissible. It is based on consecutive power-line interference subtraction and wavelet-domain electromyographic noise suppression, modified to match the strict criteria for detecting low-amplitude His potentials. The beat-to-beat wavelet-domain Wiener filtering uses a pilot signal estimate obtained from a limited number (around 20) of heart beat averages. The method resulted in an improvement of more than 4.5 dB in the signal-to-noise ratio and more than 20% reduction in mean absolute error, both measured along the P-Q segment. It is applicable for ECG signals contaminated by moderate electromyogram noise, with an initial signal-to-noise ratio of 15 dB or higher.

In this contribution, we propose a near-lossless compression algorithm for Color Filter Arrays (CFA) images. It allows higher compression ratio than any strictly lossless algorithm for the price of some small and controllable error. In our approach a structural transformation is applied first in order to pack the pixels of the same color in a structure appropriate for the subsequent compression algorithm. The transformed data is compressed by a modified version of the JPEG-LS algorithm. A nonlinear and adaptive error quantization function is embedded in the JPEG-LS algorithm after the fixed and context adaptive predictors. It is step-like and adapts to the base signal level in such a manner that higher error values are allowed for lighter parts with no visual quality loss. These higher error values are then suppressed by gamma correction applied during the image reconstruction stage. The algorithm can be adjusted for arbitrary pixel resolution, gamma value and allowable error range. The compression performance of the proposed algorithm has been tested for real CFA raw data. The results are presented in terms of compression ratio versus reconstruction error and the visual quality of the reconstructed images is demonstrated as well.