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

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Kaiserschnitt und Sterilisationtials, encountered in clinical routines. For this purpose, the chapter will be divided into two main sections. In Sect. ., we will consider the basis of bipotential recording (i.e., electrodes, artifact rejection and safety). In the second section, some general properties of a set of biosignals, wil

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überwachungstechniken in der Geburtshilfech physiological information, the acquisition protocol is first described. Then new results on heart rate variability estimation, using parametric and non parametric models are given, showing in the time-frequency plane the evolutions of cardiac and respiratory frequencies, together with the pedalli

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https://doi.org/10.1007/978-3-658-00818-5: hidden Markov models and hidden Markov trees. The HMM is a stochastic state machine which models a beat sequence as a cyclostationary Markovian process. It offers the advantage of performing both beat modeling and classification through a unique statistical approach. The HMT exploits the persisten

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https://doi.org/10.1007/978-3-658-00818-5G and ECG, as well as EOG and respiration signals. Most existing studies have focused on using the EEG as the sole information source in automatic seizure detection. Some of these studies concluded that the information obtained from the EEG should be supplemented by other information obtained from o

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Patientensteuerung im Krankenhaus,y of our tracking algorithm is to maximize the oscillatory behavior of its output rather than its spectral power, which shows interesting properties for the observation of neuronal oscillations. In addition, the structure of the filter bank allows for efficiently tracking multiple frequency componen

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