A Survey of Pre-processing Techniques UsingWavelets and Empirical-Mode Decomposition on Biomedical Signals

Recorded biomedical statistics are utilized for predicting various syndromes in humans. Recorded electrical activity of heart can be used for predicting cardiovascular ailment likelihood. Several steps are involved to process biomedical signals, among which the first step related to pre-processing, in which a noisy signal is processed for generating noise-free signal, which can be utilized for further operations. This work gives a detailed understanding of de-noising techniques those have been used for the last decade, for cardiac signals. These techniques utilize the benefits of discrete wavelet transforms (DWT), Bayesian approach, singular value decomposition (SVD), artificial neural networks (ANN), empirical-mode decomposition (EMD), adaptive filtering, and finite impulse response (FIR) filtering. These techniques have been implemented for de-noising of biosignals, individually as well as combining with other techniques, for better results.