By Amine Nait-Ali
Through 17 chapters, this ebook offers the primary of many complicated biosignal processing strategies. After an incredible bankruptcy introducing the most biosignal houses in addition to the newest acquisition thoughts, it highlights 5 particular components which construct the physique of this booklet. every one half issues essentially the most intensively used biosignals within the scientific regimen, particularly the Electrocardiogram (ECG), the Elektroenzephalogram (EEG), the Electromyogram (EMG) and the Evoked strength (EP). furthermore, each one half gathers a definite variety of chapters with regards to research, detection, type, resource separation and have extraction. those points are explored by way of a variety of complicated sign processing ways, specifically wavelets, Empirical Modal Decomposition, Neural networks, Markov types, Metaheuristics in addition to hybrid methods together with wavelet networks, and neuro-fuzzy networks.
The final half, matters the Multimodal Biosignal processing, during which we current diverse chapters with regards to the biomedical compression and the information fusion.
Instead establishing the chapters via methods, the current publication has been voluntarily dependent in accordance with sign different types (ECG, EEG, EMG, EP). This is helping the reader, attracted to a selected box, to assimilate simply the innovations devoted to a given type of biosignals. moreover, so much of signs used for representation function during this e-book will be downloaded from the clinical Database for the evaluate of photograph and sign Processing set of rules. those fabrics help significantly the person in comparing the performances in their constructed algorithms.
This e-book is fitted to ultimate 12 months graduate scholars, engineers and researchers in biomedical engineering and training engineers in biomedical technology and scientific physics.
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Additional info for Advanced Biosignal Processing
The method requires the prior R-peak detection of the desired or the interfering signal, but can be used to enhance or suppress ECG-correlated signals. 1 Contrast Functions, Independence and Non-Gaussianity As recalled in the preceding section, BSS can be performed if the sources present sufficient spectral diversity. Alternatively, time coherence may be ignored and the property of independence may be exploited up to orders higher than two, leading to the concept of independent component analysis (ICA).
Moreover, the stronger the first source, the better it will be suppressed in the estimation of the second source. Thoracic leads record strong MECG signals and will thus improve their cancellation from the FECG. The argument is similar to Widrow’s approach, where reference leads must be clean from the desired signal in order to enhance the interference suppression and prevent the signal of interest being cancelled from the filter input. In the AA extraction problem, however, the transfer vector orthogonality is more difficult to achieve, due to the spatial proximity of the atrial and ventricular sources.
The chapter concludes with some of these recent lines of research aiming to improve the performance of BSS in specific biomedical applications. The first part of the chapter (Sects. 6) is mainly addressed to readers who have little or no familiarity with the topic of source separation, but could also be useful to more experienced practitioners as a brief reference and an introduction to ECG applications of BSS. The second part (Sect. 7) is devoted to more recent developments that should be of interest to readers acquainted with the fundamentals of BSS.