Abstract
Biomedical signals are mainly employed to detect or diagnose specific pathological or physiological conditions. Furthermore, these signals are used to analyze and model biological systems in biomedical research. The goals of signal processing are signal denoising, precise recognition of signal model through analysis, feature extraction and dimension reduction for decisive function or dysfunction, and prediction of future pathological or functional events by employing machine learning techniques. Generally, the recorded biomedical signal is a mixture of signal and noise. Noise can come from instrumentation such as sensors, amplifiers, filters, etc., or from electromagnetic interference. Consequently, diverse conditions and assumptions exist for noise characteristics, which affect making a suitable choice for the signal-processing method. The aim of this chapter is to assist researchers or biomedical engineers in choosing a suitable signal analysis method and then guide them for optimal strategy by employing publicly available biomedical signal databases. Hence the fundamental signal-processing techniques utilized in the analysis of biomedical signals are discussed in this chapter. Toward the end of each section, proper MATLAB functions with different biomedical signal applications will be illustrated with an example.
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