Morphological Component Analysis for Biological Signals

Abstract

This chapter explains the removal of artifacts from the multi-resource biological signals. Morphological components can be used to distinguish between the brain activities and artifacts that are contaminated with each other in many physical situations. In this chapter, a two-stage wavelet shrinkage and morphological component analysis (MCA) for biological signals is a sophisticated way to analyze the brain activities and validate the effectiveness of artifacts removal. The source components in the biological signals can be characterized by specific morphology and measures the independence and uniqueness of the source components. Undecimated wavelet transform (UDWT), discrete cosine transform (DCT), local discrete cosine transform (LDCT), discrete sine transform (DST), and DIRAC are the orthonormal bases function used to build the explicit dictionary for the decomposition of source component of the biological signal in the morphological component analysis. The chapter discusses the implementation and optimization algorithm of the morphological component analysis.