Cuesta D, Varela M, Miró P, Galdós P, Abásolo D, Hornero R, Aboy M
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Alcoi, Spain.
Med Biol Eng Comput. 2007 Jul;45(7):671-8. doi: 10.1007/s11517-007-0200-3. Epub 2007 Jun 5.
Body temperature is a classical diagnostic tool for a number of diseases. However, it is usually employed as a plain binary classification function (febrile or not febrile), and therefore its diagnostic power has not been fully developed. In this paper, we describe how body temperature regularity can be used for diagnosis. Our proposed methodology is based on obtaining accurate long-term temperature recordings at high sampling frequencies and analyzing the temperature signal using a regularity metric (approximate entropy). In this study, we assessed our methodology using temperature registers acquired from patients with multiple organ failure admitted to an intensive care unit. Our results indicate there is a correlation between the patient's condition and the regularity of the body temperature. This finding enabled us to design a classifier for two outcomes (survival or death) and test it on a dataset including 36 subjects. The classifier achieved an accuracy of 72%.
体温是许多疾病的经典诊断工具。然而，它通常被用作简单的二元分类函数（发热或不发热），因此其诊断能力尚未得到充分发挥。在本文中，我们描述了如何利用体温规律性进行诊断。我们提出的方法基于以高采样频率获取准确的长期温度记录，并使用规律性度量（近似熵）分析温度信号。在本研究中，我们使用从重症监护病房收治的多器官功能衰竭患者获取的温度记录来评估我们的方法。我们的结果表明，患者的病情与体温规律性之间存在相关性。这一发现使我们能够设计一个针对两种结果（生存或死亡）的分类器，并在一个包含36名受试者的数据集上进行测试。该分类器的准确率达到了72%。
