Yarım Bant Nyquist Filtre ile Sinyallerin Ayrıştırılması ve Gürültünün Giderilmesi Üzerine Bir Araştırma

Abstract

Medical signal processing is often used for analyzing and detecting diseases from bio-signals. Segmentation, filtering, and noise removal are application examples in this area. The segmentation or decomposition of signals is investigated in this study. Each branch of two band quadrature-mirror orthogonal filter bank structure (analysis and synthesis parts are cascaded) is a half band Nyquist filter when down-up samplers are removed. To perform partitioning, one branch of the filter bank was adapted to a component of input signal to suppress the other element and filter out the target part of the signal. This was done by obtaining the filter-tap weights, which minimize - norm of error at the output. This goal was achieved by employing the exhaustive search method. The filter performance was tested for three scenarios: filtering with and without down-up samplers and wavelet de-noising using one-level decomposition. The comparison was done with Daubechies and Symlet filters. The approach was run and tested for separating synthetically generated evoked potential from noise, and the results show that the designed filter achieves lower total absolute error than the classical wavelet filters.