Department of Human, Information and Life Sciences, School of Health Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan.
Department of Physiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan.
PLoS One. 2022 Jun 28;17(6):e0270626. doi: 10.1371/journal.pone.0270626. eCollection 2022.
Suppressing the elevation in core body temperature is an important factor in preventing heatstroke. However, there is still no non-invasive method to sense core body temperature. This study proposed an algorithm that estimates core body temperature based on electrocardiogram signals. A total of 12 healthy men (mean age ± SD = 39.6 ± 13.4) performed an ergometric exercise load test under two conditions of exercise load in an environmental chamber adjusted to a temperature of 35°C and humidity of 50%. Vital sensing data such as electrocardiograms, core body temperatures, and body surface temperatures were continuously measured, and physical data such as body weight were obtained from participants pre- and post-experiment. According to basic physiological knowledge, heart rate and body temperature are closely related. We analyzed the relationship between core body temperature and several indexes obtained from electrocardiograms and found that the amount of change in core body temperature had a strong relationship with analyzed data from electrocardiograms. Based on these findings, we developed the amount of change in core body temperature estimation model using multiple regression analysis including the Poincaré plot index of the ECG R-R interval. The estimation model showed an average estimation error of -0.007°C (average error rate = -0.02%) and an error range of 0.457-0.445°C. It is suggested that continuous core body temperature change can be estimated using electrocardiogram signals regardless of individual characteristics such as age and physique. Based on this applicable estimation model, we plan to enhance estimation accuracy and further verify efficacy by considering clothing and environmental conditions.
抑制核心体温升高是预防中暑的一个重要因素。然而，目前仍然没有非侵入性的方法来感知核心体温。本研究提出了一种基于心电图信号估计核心体温的算法。共有 12 名健康男性（平均年龄 ± 标准差=39.6 ± 13.4）在两种环境下进行了一项力竭运动负荷试验，环境舱内温度设定为 35°C，湿度为 50%。连续测量了心电图、核心体温和体表温度等生命体征数据，并在实验前后从参与者处获得了体重等身体数据。根据基本生理知识，心率和体温密切相关。我们分析了核心体温与心电图获得的几个指标之间的关系，发现核心体温的变化量与心电图的分析数据有很强的相关性。基于这些发现，我们使用包括心电图 R-R 间期 Poincaré 图指数在内的多元回归分析开发了核心体温变化量的估计模型。该估计模型的平均估计误差为-0.007°C（平均误差率=-0.02%），误差范围为 0.457-0.445°C。这表明可以使用心电图信号来估计连续的核心体温变化，而不受年龄和体型等个体特征的影响。基于这个适用的估计模型，我们计划通过考虑服装和环境条件来提高估计精度并进一步验证其效果。
