Neural network application in strange attractor investigation to detect a FGD

Y. Z. Mehran, A. Nasrabadi
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引用次数: 1

Abstract

There is growing interest in modeling and processing nonlinear behavior in the biological systems. In this paper we applied such methods for detecting Functional Disorder in Gastric. Conventional tools for analyzing such data use information from the power spectral density of the time series, and hence are restricted to little information of data. This information does not provide a sufficient representation of a signal with strong nonlinear properties. In this work, we attempt to extract various nonlinear dynamical invariants of the underlying attractor from the signals. We show that these invariants can discriminate between normal and Functional Gastrointestinal Disorders (FGD) classes.
神经网络在奇异吸引子检测中的应用
人们对生物系统非线性行为的建模和处理越来越感兴趣。本文将这些方法应用于胃功能障碍的检测。分析此类数据的传统工具使用来自时间序列的功率谱密度信息,因此受到数据信息很少的限制。该信息不能充分表示具有强非线性特性的信号。在这项工作中,我们试图从信号中提取潜在吸引子的各种非线性动态不变量。我们表明这些不变量可以区分正常和功能性胃肠疾病(FGD)类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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