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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern
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Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern

机译：2型糖尿病患者运动程序中肌肉近红外光谱（NIRS）信号的熵分析：肌肉代谢模式的定量评估

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摘要

Diabetes mellitus (DM) is a metabolic disorder that is widely rampant throughout the world population these days. The uncontrolled DM may lead to complications of eye, heart, kidney and nerves. The most common type of diabetes is the type 2 diabetes or insulin-resistant DM. Near-infrared spectroscopy (NIRS) technology is widely used in non-invasive monitoring of physiological signals. Three types of NIRS signals are used in this work: (i) variation in the oxygenated haemoglobin (O2Hb) concentration, (ii) deoxygenated haemoglobin (HHb), and (iii) ratio of oxygenated over the sum of the oxygenated and deoxygenated haemoglobin which is defined as: tissue oxygenation index (TOI) to analyze the effect of exercise on diabetes subjects.The NIRS signal has the characteristics of non-linearity and non-stationarity. Hence, the very small changes in this time series can be efficiently extracted using higher order statistics (HOS) method. Hence, in this work, we have used sample and HOS entropies to analyze these NIRS signals. These computer aided techniques will assist the clinicians to diagnose and monitor the health accurately and easily without any inter or intra observer variability.Results showed that after a one-year of physical exercise programme, all diabetic subjects increased the sample entropy of the NIRS signals, thus revealing a better muscle performance and an improved recruitment by the central nervous system. Moreover, after one year of physical therapy, diabetic subjects showed a NIRS muscular metabolic pattern that was not distinguished from that of controls.We believe that sample and bispectral entropy analysis is need when the aim is to compare the inner structure of the NIRS signals during muscle contraction, particularly when dealing with neuromuscular impairments.
机译：糖尿病（DM）是一种新陈代谢疾病，近来在世界各地的人群中普遍盛行。失控的糖尿病可能导致眼睛，心脏，肾脏和神经的并发症。糖尿病最常见的类型是2型糖尿病或胰岛素抵抗性DM。近红外光谱（NIRS）技术被广泛用于生理信号的非侵入式监测。这项工作使用了三种类型的NIRS信号：（i）氧合血红蛋白（O2Hb）浓度的变化，（ii）氧合血红蛋白（HHb），以及（iii）氧合占氧合和氧合血红蛋白总和的比率NIRS信号定义为：组织氧合指数（TOI），用于分析运动对糖尿病患者的影响。NIRS信号具有非线性和非平稳性的特征。因此，可以使用高阶统计（HOS）方法有效地提取此时间序列中的很小变化。因此，在这项工作中，我们使用样本和HOS熵来分析这些NIRS信号。这些计算机辅助技术将帮助临床医生准确，轻松地诊断和监测健康状况，而无观察者之间或观察者之间的差异。结果表明，经过一年的体育锻炼计划，所有糖尿病患者均增加了NIRS信号的样本熵，因此显示出更好的肌肉性能和中枢神经系统的改善的募集。此外，经过一年的理疗，糖尿病患者表现出与对照组无差异的NIRS肌肉代谢模式。我们认为，在比较期间NIRS信号的内部结构时，需要进行样本和双谱熵分析肌肉收缩，尤其是在处理神经肌肉损伤时。

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