الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Heart rate variability (HRV) refers to variations in heart complex wave beat tobeat intervals. The HRV is a reliable reflection of many physiological, psychological, and environmental factors modulating the normal rhythm of the heart. In fact, the HRV seriously provides a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. However, the HRV has a periodicity that is important for monitoring and following up the cases. The structure generating heart complex wave signal is not simply linear, but it also involves nonlinear contributions. These contributions are totally correlated. The HRV is stochastic and chaotic (stochaotic) signal. It has utmost importance in heart diseases diagnosis, and it needs a sensitive tool to analyze its variability. In early works, Rosenstein and Wolf had used the Lyapunov exponent (LE) as a quantitative measure for HRV detection sensitivity, but the Rosenstein and Wolf methods diverge in determining the main features of HRV sensitivity, while Mazhar- Eslam introduced a modification algorithm to overcome the Rosenstein and Wolf drawbacks. The present work introduces and discusses some methods to be used for analysis and prediction of HRV. Also, explain a novel reliable methods to analyze the linear and nonlinear behaviour ofthe heart complex wave variability, to assess the use of the HRV as a versatile tool for heart disease diagnosis. This study displays a declaration for the concept of the Lyapunov Exponent (LE) parameters to be used for HRV diagnosis and proposes a modified algorithm for parameters computations. The Mazhar-Eslam Variability Frequency MVF ”OM”is the most versatile tool for HRV prediction and diagnosis that discussed in this thesis. The MVF ”OM” diversity of initially closely trajectories in state-space is connected with folding of them. The presence of a positive part MVF for all initial conditions in a restricted dynamical system, is the vastly used definition of deterministic chaos. Thus, to distinguish between periodic signals chaotic and dynamics, the MVF OM are predominantly used. The trajectories of chaotic signals in state-space pursue typical patterns. Nearly diverge trajectories diverge and converge exponentially, proportional to each other. A negative MVF means that the orbit entices to a settled point or stable periodic orbit. Negative MVFs are distinguishing of non-fogyish systems. Like systems display asymptotic stability. For more stability, the MVF is more negative. When MVF tends to infinity, it is mean the excessive stable periodicity. Thus, it is clear that the MVF is the most suitable and sensitive tool for predicting the HRV. So, in present work in this thesis it would be used to predict and verify the importance of HRV stochaotic periodicity. Also, it was stated that the positive part indicates case status and the negative part indicates the stability and periodicity. This explains the necessity to consider both polarities (positive and negative) of the MVF. Also, it was stated that in the present work the stochaotic periodicity and variation in the HRV the negative and positive MVFs OMs should be taken in account. Thus, a new approach to be defined as Bipolar Mazhar-Eslam Variability Frequency BMVF method is introduced as a great role in monitoring, predicting and diagnosing the HRV stochaotic signal. The BMVF has ability to monitor and follow up the patient case. It shows the disease by the positive BMVF part and shows the periodicity by the negative BMVF part. In addition to, gives an insight on a modified Poincare plot as a non-linear technique that analyse HRV. Morever, it discusses the Poincare limitation, cause of standard deviation SDI, S02 and how to overcome this limitation by using complex correlation measure (CCM). The CCM is most sensitive to changes in temporal structure of the Poincare plot as compared to SD I and SD2.