Performance of intensity-based non-normalized pointwise algorithms in dynamic speckle analysis.

Intensity-based pointwise non-normalized algorithms for 2D evaluation of activity in optical metrology with dynamic speckle analysis are studied and compared. They are applied to a temporal sequence of correlated speckle patterns formed at laser illumination of the object surface. Performance of each algorithm is assessed through the histogram of estimates it produces. A new algorithm is proposed that provides the same quality of the 2D activity map for less computational effort. The algorithms are applied both to synthetic and experimental data.

Beat-to-beat noise removal in non-invasive His-bundle electrocardiogram.

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.

Suppression of electromyogram interference on the electrocardiogram by transform domain denoising.

A method for suppression of electromyogram (EMG) interference in electrocardiogram (ECG) recordings is presented. By assuming that the EMG is long-term non-stationary Gaussian noise, two successive decompositions were proposed, and the data transformed for Wiener filtering. Successive ECG cycles were rearranged and aligned by the R-wave, forming a matrix containing separated heart cycles in its rows. A short-window discrete cosine transform (DCT) was applied to the columns of the matrix for inter-cycle de-correlation. Next, Wiener filtering in a translation-invariant wavelet domain was performed on the DCT-transformed matrix rows for de-correlation of the data into each ECG cycle. The method resulted in an improvement in the signal-to-noise ratio of more than 10 dB, a threefold reduction in mean relative amplitude errors and reduced ripple artifacts around the signal transients, thus preserving the waveform in diagnostically important signal segments.