A Parametric Framework for Modelling of Bioelectrical Signals [electronic resource] /
by Yar M. Mughal.
- XV, 81 p. 42 illus., 5 illus. in color. online resource.
- Series in BioEngineering, 2196-8861 .
- Series in BioEngineering, .
Introduction 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.
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).
9789812879691
10.1007/978-981-287-969-1 doi
Engineering. Cardiac imaging. Respiratory organs--Diseases. Medical physics. Radiation. Biomedical engineering. Engineering. Biomedical Engineering. Cardiac Imaging. Medical and Radiation Physics. Signal, Image and Speech Processing. Pneumology/Respiratory System.