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Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals

机译：经验模式分解在正常和糖尿病患者RR间隔信号分析中的应用

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摘要

Large number of people are affected by Diabetes Mellitus (DM) which is difficult to cure due to its chronic nature and genetic link. The uncontrolled diabetes may lead to heart related problems. Therefore, the diagnosis and monitoring of diabetes is of great importance. The automatic detection of diabetes can be performed using RR-interval signals. The RR-interval signals are nonlinear and non-stationary in nature. Hence linear methods may not be able to capture the hidden information present in the signal. In this paper, a new nonlinear method based on empirical mode decomposition (EMD) is proposed to discriminate between diabetic and normal RR-interval signals. The mean frequency parameter using Fourier Bessel series expansion (MFFB) and the two bandwidth parameters namely, amplitude modulation bandwidth (B-AM) and frequency modulation bandwidth (B-FM) extracted from the intrinsic mode functions (IMFs) obtained from the EMD of RR-interval signals are used to discriminate the two groups. Unique representations such as analytic signal representation (ASR) and second order difference plot (SODP) for IMFs of RR-interval signals are also proposed to differentiate the two groups. The area parameters are computed from ASR and SODP of IMEs of RR-interval signals. Area computed from these representation as area corresponding to the 95% central tendency measure (CTM) of ASR of IMFs (A(ASR)) and 95% confidence ellipse area of SODP of IMF (A(SODP)) are also proposed to discriminate diabetic and normal RR-interval signals. Overall, five features are extracted from IMFs of RR-interval signals namely MFFB, B-AM, B-FM, A(ASR) and A(SODP). Kruskal Wallis statistical test is used to measure the discrimination ability of the proposed features for detection of diabetic RR-interval signals. Results obtained from proposed methodology indicate that these features provide the statistically significant difference between diabetic and normal classes. (C) 2015 Elsevier Ltd. All rights reserved.
机译：糖尿病（DM）影响着许多人，由于其长期的性质和遗传联系，它很难治愈。不受控制的糖尿病可能导致心脏相关问题。因此，糖尿病的诊断和监测非常重要。可以使用RR间隔信号执行糖尿病的自动检测。 RR间隔信号本质上是非线性且非平稳的。因此，线性方法可能无法捕获信号中存在的隐藏信息。本文提出了一种基于经验模态分解（EMD）的新非线性方法，用于区分糖尿病患者和正常RR间隔信号。使用傅里叶贝塞尔级数展开（MFFB）的平均频率参数和两个带宽参数，即从从EMD的固有模式函数（IMF）中提取的振幅调制带宽（B-AM）和频率调制带宽（B-FM） RR间隔信号用于区分两组。还提出了独特的表示方法，例如RR间隔信号的IMF的分析信号表示（ASR）和二阶差分图（SODP），以区分两组。面积参数是根据RR间隔信号的IME的ASR和SODP计算得出的。还建议从这些表示形式计算出的面积对应于IMF的ASR（A（ASR））的95％集中趋势度量（CTM）和IMF的SODP的95％置信椭圆面积（A（SODP））来区分糖尿病和正常的RR间隔信号。总体而言，从RR间隔信号的IMF中提取了五个特征，即MFFB，B-AM，B-FM，A（ASR）和A（SODP）。使用Kruskal Wallis统计检验来测量所提议特征在检测糖尿病RR间隔信号方面的辨别能力。从提出的方法学中获得的结果表明，这些特征在糖尿病和正常人群之间提供了统计学上的显着差异。 （C）2015 Elsevier Ltd.保留所有权利。

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  • 来源
    《Expert Systems with Application》 |2015年第9期|4567-4581|共15页
  • 作者

    Pachori Ram Bilas; Avinash Pakala; Shashank Kora; Sharma Rajeev; Acharya U. Rajendra;

  • 作者单位

    Indian Inst Technol lndore, Discipline Elect Engn, Indore 452017, Madhya Pradesh, India;

    Indian Inst Technol lndore, Discipline Elect Engn, Indore 452017, Madhya Pradesh, India;

    Indian Inst Technol lndore, Discipline Elect Engn, Indore 452017, Madhya Pradesh, India;

    Indian Inst Technol lndore, Discipline Elect Engn, Indore 452017, Madhya Pradesh, India;

    Ngee Ann Polytech, Dept Elect & Commun Engn, Singapore 599489, Singapore;

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  • 正文语种 eng
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  • 关键词

    RR signal; Diabetes; EMD; IMF; Nonlinear;

    机译：RR信号;糖尿病;EMD;IMF;非线性;

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