Cardiac signals have complex structures representing a combination of simpler structures. In this paper, we develop a new data analytic tool that can extract the complex structures of cardiac signals using the framework of multi-chaotic analysis, which is based on the p-norm for calculating the largest Lyapunov exponent (LLE). Appling the p-norm is useful for deriving the spectrum of the generalized largest Lyapunov exponents (GLLE), which is characterized by the width of the spectrum (which we denote by W). This quantity measures the degree of multi-chaos of the process and can potentially be used to discriminate between different classes of cardiac signals. We propose the joint use of the GLLE and spectrum width to investigate the multi-chaotic behavior of inter-beat (R-R) intervals of cardiac signals recorded from 54 healthy subjects (hs), 44 subjects diagnosed with congestive heart failure (chf), and 25 subjects diagnosed with atrial fibrillation (af). With the proposed approach, we build a regression model for the diagnosis of pathology. Multi-chaotic analysis showed a good performance, allowing the underlying dynamics of the system that generates the heart beat to be examined and expert systems to be built for the diagnosis of cardiac pathologies.

中文翻译：

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用于区分正常和病理类别的心脏信号中节拍间 (RR) 间隔的多混沌分析

心脏信号具有代表较简单结构组合的复杂结构。在本文中，我们开发了一种新的数据分析工具，可以使用多混沌分析框架提取心脏信号的复杂结构，该框架基于计算最大李雅普诺夫指数 (LLE) 的 p 范数。应用 p 范数对于推导广义最大李雅普诺夫指数 (GLLE) 的频谱非常有用，GLLE 的特征在于频谱的宽度（我们用 W 表示）。该量测量过程的多混沌程度，并可能用于区分不同类别的心脏信号。我们建议联合使用 GLLE 和频谱宽度来研究从 54 名健康受试者 (hs) 记录的心脏信号的搏动 (RR) 间隔的多混沌行为，44 名受试者被诊断患有充血性心力衰竭 (chf)，25 名受试者被诊断患有心房颤动 (af)。使用所提出的方法，我们建立了一个用于病理诊断的回归模型。多混沌分析显示出良好的性能，允许检查产生心跳的系统的潜在动力学，并为心脏病的诊断建立专家系统。