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Autor(en): 
  • Yar M. Mughal
  • A Parametric Framework for Modelling of Bioelectrical Signals 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Dezember 2018  
    Genre:  Naturwissensch., Medizin, Technik 
     
    B / Biomedical engineering / Biomedical Engineering and Bioengineering / Biophysics / Cardiac Imaging / Digital and Analog Signal Processing / engineering / Image processing / Imaging systems & technology / INTERNAL MEDICINE / Medical and Radiation Physics / Medical imaging / Medical physics / Nuclear chemistry, photochemistry & radiation / Pneumology/Respiratory System / RADIATION / Radiology / Respiratory medicine / Respiratory organs—Diseases / Signal Processing / Signal, Image and Speech Processing / Speech processing systems
    ISBN:  9789811357350 
    EAN-Code: 
    9789811357350 
    Verlag:  Springer Nature EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Series in BioEngineering  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  169 gr 
    Seiten:  81 
    Illustration:  XV, 81 p. 42 illus., 5 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  Previously published in hardcover 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    This book examines non-invasive, electrical-based methods for disease diagnosis and assessment of heart function. In particular, a formalized signal model is proposed since this offers several advantages over methods that rely on measured data alone. By using a formalized representation, the parameters of the signal model can be easily manipulated and/or modified, thus providing mechanisms that allow researchers to reproduce and control such signals. In addition, having such a formalized signal model makes it possible to develop computer tools that can be used for manipulating and understanding how signal changes result from various heart conditions, as well as for generating input signals for experimenting with and evaluating the performance of e.g. signal extraction methods. The work focuses on bioelectrical information, particularly electrical bio-impedance (EBI). Once the EBI has been measured, the corresponding signals have to be modelled for analysis. This requires a structured approach in order to move from real measured data to the model of the corresponding signals. This book proposes a generic framework for this procedure. It can be used as a guide for modelling impedance cardiography (ICG) and impedance respirography (IRG) signals, as well as for developing the corresponding bio-impedance signal simulator (BISS).

      



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