New PDF release: Advanced Biosignal Processing

By Amine Nait-Ali

Through 17 chapters, this ebook offers the primary of many complicated biosignal processing concepts. After a big bankruptcy introducing the most biosignal houses in addition to the latest acquisition innovations, it highlights 5 particular components which construct the physique of this e-book. each one half matters some of the most intensively used biosignals within the medical 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, type, resource separation and have extraction. those facets are explored by way of quite a few complex sign processing techniques, particularly wavelets, Empirical Modal Decomposition, Neural networks, Markov versions, Metaheuristics in addition to hybrid methods together with wavelet networks, and neuro-fuzzy networks.

The final half, issues the Multimodal Biosignal processing, within which we current assorted chapters concerning the biomedical compression and the information fusion.

Instead establishing the chapters by means of techniques, the current booklet has been voluntarily dependent based on sign different types (ECG, EEG, EMG, EP). This is helping the reader, attracted to a particular box, to assimilate simply the suggestions devoted to a given type of biosignals. additionally, so much of signs used for representation function during this booklet might be downloaded from the scientific Database for the overview of snapshot and sign Processing set of rules. those fabrics help significantly the person in comparing the performances in their built algorithms.

This booklet is suited to ultimate 12 months graduate scholars, engineers and researchers in biomedical engineering and training engineers in biomedical technological know-how and scientific physics.

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1 Source Statistical Characterization In many biomedical applications, some statistical features of the source(s) of interest may be known in advance. The AA waveform in atrial flutter episodes typically shows a sawtooth shape that can be characterized as a sub-Gaussian distribution, and becomes near-Gaussian in more disorganized states of AF observed as the disease evolves. In the separation of the FECG from maternal skin recordings, the sources of interest, the fetal heartbeat signals, are usually impulsive and thus present heavy tails in their pdfs; they can be considered as super-Gaussian random variables.

14). ), the BSSR method has the potential of providing algebraically a good initial approximation of the desired signal. Depending of the quality of the reference, this initial estimate may be refined by later processing. The prior information can be incorporated explicitly within the ICA update by means of appropriate constraints on the contrast function. This idea gives rise to a general framework called constrained ICA (cICA) [42, 43]. When prior knowledge is expressed in terms of reference signals, the approach is referred to as ICA with 2 Extraction of ECG Characteristics Using Source Separation Techniques 41 reference (ICA-R) and can be mathematically cast into the constrained optimization problem: maximize ⌿(y) subject to ε(y) ≤ ξ .

The PCA of observed vector x(t) can be briefly expressed as: 1. Find vector w1 maximizing E{y12 (t)}, with y1 (t) = wT1 x(t), subject to ||w1 ||2 = 1. 2. For k = 2, 3, . , n: Find vector wi maximizing E{yi2 (t)}, with yi (t) = wiT x(t), subject to ||wi ||2 = 1 and wiT w j = 0, j = 1, . , i – 1. As noted in [66, 27], each wk represents a spatial filter orthogonal to {w1 , w2 , . , wk –1 } whose output has maximal power. It is well known that the solution to the above problem is given by the eigenvalue decomposition (EVD) of the observation covariance matrix Rx = E{x(t)x(t)T }.

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