Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals
SubjectSensing, amplification; antialiasing; Analog-to-digital conversion; filtering; Fourier transform; Periodogram; Welch; Autoregressive (AR); Yule-Walker; Burg; Covariance; Modified covariance; Subspace-based methods; MUSIC; Eigenvector; Time–frequency analysis; Short time Fourier transform (STFT); Wavelet analysis; Continuous wavelet transform (CWT); Discrete wavelet transform (DWT); Empirical mode decomposition (EMD)
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AbstractThe primary purposes of the biomedical signals are the detection or diagnosis of disease or physiological states. These signals are also employed in biomedical research to model and study biological systems. The objective of the signal acquisition, pre-conditioning and feature extraction is to attain a precise realization of model or recognition of decisive elements or malfunctioning of human corporal systems using machine or deep learning. Furthermore, it allows future clinical or physiological events to be predicted using machine and deep learning. The obtained biological signal is frequently a complex combination of noise, artifacts, and signal. Instrumentation using sensors, amplifiers, filters, and analog-to-digital converters can produce artifacts. The muscular activities can introduce interference and the powerline and electromagnetic emissions are considered as the primary sources of noise. A good choice of signal collection and processing techniques may be made as a consequence of intended design specifications. This chapter aims to familiarize scientists and biomedical engineers with potential feature extraction methods and in comprehending the fundamentals of the signal acquisition and processing chain.
DepartmentElectrical and Computer Engineering
Book titleAdvances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning