Digital filter design
Discrete-time signal processing
Discrete-time signal processing
Guide to Neural Computing Applications
Guide to Neural Computing Applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Virtual Bio-Instrumentation: Biomedical, Clinical, and Healthcare Applications in LabVIEW with CD-ROM
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In this paper novel compression techniques are developed for portable heart-monitoring equipment that could also form the basis for more intelligent diagnostic systems thanks to the way the compression algorithms depend on signal classification. There are two main categories of compression which are employed for electrocardiogram signals: lossless and lossy. Design of an optimal Wiener filter is implemented to remove noise from a signal, considering that the signal is statistically stationary and the noise is a stationary random process that is statistically independent of the signal. Two programs for compression and Wiener optimal filtering are realized in MATLAB. The main idea of optimal filtering is to give bigger weight coefficients to signal spectra parts where signal noise has less power and true signal spectral components have bigger power. A Savitzky-Golay filtering is applied to a noisy electrocardiogram and a comparison is done between the four methods Wiener, Butterworth, Savitzky-Golay and synchronized averaging.