By Amine Nait-Ali
Through 17 chapters, this e-book offers the primary of many complex biosignal processing ideas. After a major bankruptcy introducing the most biosignal houses in addition to the latest acquisition strategies, it highlights 5 particular elements which construct the physique of this publication. each one half matters some of the most intensively used biosignals within the scientific regimen, specifically the Electrocardiogram (ECG), the Elektroenzephalogram (EEG), the Electromyogram (EMG) and the Evoked capability (EP). furthermore, every one half gathers a undeniable variety of chapters concerning research, detection, class, resource separation and have extraction. those elements are explored via a number of complex sign processing techniques, specifically wavelets, Empirical Modal Decomposition, Neural networks, Markov types, Metaheuristics in addition to hybrid ways together with wavelet networks, and neuro-fuzzy networks.
The final half, issues the Multimodal Biosignal processing, during which we current assorted chapters relating to the biomedical compression and the knowledge fusion.
Instead setting up the chapters via ways, the current booklet has been voluntarily based in accordance with sign different types (ECG, EEG, EMG, EP). This is helping the reader, drawn to a particular box, to assimilate simply the strategies devoted to a given classification of biosignals. moreover, so much of signs used for representation function during this ebook will be downloaded from the clinical Database for the overview of snapshot and sign Processing set of rules. those fabrics support significantly the consumer in comparing the performances in their built algorithms.
This ebook is suited to ultimate yr graduate scholars, engineers and researchers in biomedical engineering and practising engineers in biomedical technology and scientific physics.
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Extra resources for Advanced Biosignal Processing
Contrast functions based on reference signals have been developed in . However, such references are defined as arbitrary unitary transformations acting on the original sources, and so they constitute a somewhat different concept than in the works reported in the above paragraphs. 3 Spatial Reference The prior knowledge about our signal extraction problem can sometimes be captured by the structure or topography of the transfer vector associated with the source of interest, rather than by the time course of a reference signal.
This result is consistent with intuition and the Central Limit Theorem: as mixing random variables tends to increase Gaussianity, one should proceed in the opposite direction, decreasing Gaussianity, to achieve their separation. 5) is shown to be linked to entropy and negentropy, both concepts in turn related with nonGaussianity [24, 15, 16]. Despite their optimality, information-theoretical contrasts such as MI or ME involve pdfs, difficult to deal with in practice. To improve the numerical tractability, pdfs can be approximated by their truncated Edgeworth or Gram-Charlier expansions around a Gaussian distribution.
For instance, the AA signal is often near-Gaussian, so that its separation from Gaussian noise and interference is compromised when relying on HOS only, as illustrated by the results of the CoM2 algorithm in Sect. 1 (Figs. 5). As noted in [33, 34], statistical independence alone is sometimes unable to produce physiologically meaningful results in biomedical signal processing applications. In these conditions, source extraction performance can be improved by taking into account additional assumptions about the signals of interest or the mixing structure other than independence.