Applying continuous chaotic modeling to cardiac signal analysis

摘要: In the last decade, chaos theory has become a popular method for approaching analysis of nonlinear data which most mathematical models produce intractable solutions. The concept was first introduced with applications in meteorology. Since then, considerable work been done theoretical aspects chaos. Applications have abounded, especially medicine and biology. A particularly active area application cardiology. Many heart disease addressed, including whether represents healthy or diseased state. Most approaches to chaotic modeling rely on discrete continuous problems, are represented by computer algorithms. Due nature models, both discretization simulation can lead propagation errors that may overtake actual solution. This article describes an approach model is developed based conjectured solution logistic equation. As result this approach, two practical methods quantifying variability sets derived. graphical representation obtained using second-order difference plots time series data. second central tendency measure (CTM) quantifies degree variability. CTM then be used as parameter decision such neural networks. It appears measuring more useful chaos, demonstrated congestive failure patients compared normal controls.