The salivary cortisol classification based on the heart rate variability.

IF 1.1 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Hormone Molecular Biology and Clinical Investigation Pub Date : 2025-04-15 DOI:10.1515/hmbci-2025-0009

Leila Simorgh, Gila Pirzad Jahromi, Sousan Salari, Boshra Hatef

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引用次数: 0

Abstract

Objectives: Stress is a physiological state that is essential for the survival of living organisms. Heart rate variability (HRV) and cortisol hormone are indicators of the stress system. According to research, it has been demonstrated that the activation of the stress system is not consciously controlled by the individual, but rather occurs subconsciously. It is a novel concept to employ HRV indexes to assess the level of cortisol concentration as a more reliable indicator of stress system activation, as opposed to relying solely on the individual's emotional state.

Methods: In order to understand the relationship between stress and cortisol secretion and its effect on electrophysiological biomarkers like HRV, the algorithms were designed using machine learning algorithms such as SVM, XGB, and MLP in the 634 adult healthy men (20-50 years old). Trait social stress test was utilized to make wide range of cortisol concentration from no to moderate stress.

Results: These algorithms classified cortisol level between 9:00 AM and 2:00 PM in the optimal (5-15 ng/mL), non (less than 5 and more than 15 ng/mL) range, using HRV indexes (12 features). The XBG algorithm could achieve best classification with an accuracy rate of 99 % and an F1 rate of 99 %. They also indicated the state of an individual's stress system by indicating the concentration level of cortisol, which is its fundamental indicator.

Conclusions: In addition to classifying stress, the HRV can also classify salivary cortisol in adult health men.

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基于心率变异性的唾液皮质醇分类。

目的：应激是生物体生存所必需的一种生理状态。心率变异性（HRV）和皮质醇激素是应激系统的指标。研究表明，压力系统的激活不是由个体有意识地控制的，而是潜意识地发生的。利用HRV指数来评估皮质醇浓度水平，作为应激系统激活的一个更可靠的指标，这是一个新的概念，而不是仅仅依赖于个人的情绪状态。方法：为了解应激与皮质醇分泌的关系及其对HRV等电生理生物标志物的影响，采用SVM、XGB、MLP等机器学习算法对634名20-50岁成年健康男性进行算法设计。采用特质社会压力测试，在无压力到中等压力的大范围内测量皮质醇浓度。结果：这些算法使用HRV指数（12个特征）将上午9:00至下午2:00之间的皮质醇水平划分为最佳（5-15 ng/mL），非（小于5和大于15 ng/mL）范围。XBG算法的分类准确率为99 %，F1率为99 %，达到最佳分类效果。他们还通过皮质醇的浓度水平来指示一个人的压力系统的状态，皮质醇是压力系统的基本指标。结论：HRV除了对应激进行分类外，还可以对成年健康男性的唾液皮质醇进行分类。

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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来源期刊

Hormone Molecular Biology and Clinical Investigation BIOCHEMISTRY & MOLECULAR BIOLOGY-

CiteScore

2.60

自引率

0.00%

发文量

期刊介绍： Hormone Molecular Biology and Clinical Investigation (HMBCI) is dedicated to the provision of basic data on molecular aspects of hormones in physiology and pathophysiology. The journal covers the treatment of major diseases, such as endocrine cancers (breast, prostate, endometrium, ovary), renal and lymphoid carcinoma, hypertension, cardiovascular systems, osteoporosis, hormone deficiency in menopause and andropause, obesity, diabetes, brain and related diseases, metabolic syndrome, sexual dysfunction, fetal and pregnancy diseases, as well as the treatment of dysfunctions and deficiencies. HMBCI covers new data on the different steps and factors involved in the mechanism of hormone action. It will equally examine the relation of hormones with the immune system and its environment, as well as new developments in hormone measurements. HMBCI is a blind peer reviewed journal and publishes in English: Original articles, Reviews, Mini Reviews, Short Communications, Case Reports, Letters to the Editor and Opinion papers. Ahead-of-print publishing ensures faster processing of fully proof-read, DOI-citable articles.