ABSTRACT
In this chapter, significant emphasis has been given to machine learning (ML) for biomedical signal processing. The basic purpose of this chapter is to explore the numerous possibilities of ML in the field of biomedical signal processing. With the increasing volume of available biomedical data, network speed, and computing power, the modern biomedical signal is facing an unique amount of data to interpret and analyze. Phenomena like Big Data-omics restricting from numerous diagnostic methods and novel multiparametric singling modalities tend to produce practically unmanageable amounts of data. Traditional ML ideas have established many limitations when it comes to correctly diagnose like electrocardiogram, hearing aid, and EEG diseases. At the same time, static graph networks are unable to capture the fluctuations in monitor and processing progress. Therefore, deep learning and artificial intelligence are applied in bio-medical singling because they excel at providing quantitative assessments of biomedical singling characteristics.
