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Titlebook: Advanced Biosignal Processing; Amine Na?t-Ali Book 2009 Springer-Verlag Berlin Heidelberg 2009 Elektroenzephalografie.Radiologieinformatio

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期刊全稱Advanced Biosignal Processing
影響因子2023Amine Na?t-Ali
視頻videohttp://file.papertrans.cn/146/145268/145268.mp4
發(fā)行地址Concise overview of most all standard method.Presents latest developments in Biosignal processing
圖書封面Titlebook: Advanced Biosignal Processing;  Amine Na?t-Ali Book 2009 Springer-Verlag Berlin Heidelberg 2009 Elektroenzephalografie.Radiologieinformatio
影響因子Generally speaking, Biosignals refer to signals recorded from the human body. They can be either electrical (e. g. Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), etc. ) or non-electrical (e. g. breathing, movements, etc. ). The acquisition and processing of such signals play an important role in clinical routines. They are usually considered as major indicators which provide clinicians and physicians with useful information during diagnostic and monitoring processes. In some applications, the purpose is not necessarily medical. It may also be industrial. For instance, a real-time EEG system analysis can be used to control and analyze the vigilance of a car driver. In this case, the purpose of such a system basically consists of preventing crash risks. Furthermore, in certain other appli- tions,asetof biosignals (e. g. ECG,respiratorysignal,EEG,etc. ) can be used toc- trol or analyze human emotions. This is the case of the famous polygraph system, also known as the “l(fā)ie detector”, the ef ciency of which remains open to debate! Thus when one is dealing with biosignals, special attention must be given to their acquisition, their analysis and their processin
Pindex Book 2009
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Basic Concepts of Behavioral Pharmacologyials (BAEPs). In the second example, the Particle Swarm Optimization Algorithms are used for curve fitting Event Related Potentials and the heart beat signal. As will be further discussed, metaheuristics can naturally be extended to various applications relevant to biosignal processing.
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https://doi.org/10.1007/978-3-658-00818-5ther recorded physiological signals. This chapter documents an approach that uses the ECG as the basis for seizure detection and explores how such approach could be combined with the EEG based methodologies to achieve a robust automatic seizure detector.
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