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020 _a9789812879691
_9978-981-287-969-1
024 7 _a10.1007/978-981-287-969-1
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC059000
_2bisacsh
072 7 _aMQW
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082 0 4 _a610.28
_223
100 1 _aMughal, Yar M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_959984
245 1 2 _aA Parametric Framework for Modelling of Bioelectrical Signals
_h[electronic resource] /
_cby Yar M. Mughal.
250 _a1st ed. 2016.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2016.
300 _aXV, 81 p. 42 illus., 5 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSeries in BioEngineering,
_x2196-887X
505 0 _aIntroduction and Motivation -- State of the Art of Modelling and Simulation of the Physiological Systems -- Proposed Novel Generic Framework for Modelling the Bioelectrical Information -- Implementation of the Framework and the Experimental Results -- Conclusions.
520 _aThis 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).
650 0 _aBiomedical engineering.
_93292
650 0 _aCardiology.
_936677
650 0 _aMedical physics.
_94451
650 0 _aSignal processing.
_94052
650 0 _aRespiratory organs—Diseases.
_959126
650 1 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aCardiology.
_936677
650 2 4 _aMedical Physics.
_94451
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aPneumology.
_959127
710 2 _aSpringerLink (Online service)
_959985
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789812879684
776 0 8 _iPrinted edition:
_z9789812879707
776 0 8 _iPrinted edition:
_z9789811357350
830 0 _aSeries in BioEngineering,
_x2196-887X
_959986
856 4 0 _uhttps://doi.org/10.1007/978-981-287-969-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80455
_d80455