解码室内温度和光线对神经活动的影响:脑电信号的熵分析。

IF 2.9 4区 医学 Q2 PHYSIOLOGY
Chiara Pappalettera, Silvia Angela Mansi, Marco Arnesano, Fabrizio Vecchio
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引用次数: 0

摘要

了解神经对温度和光线等室内特征的反应对于理解物理环境如何影响人脑至关重要。我们的研究引入了一种创新方法,利用熵分析,特别是近似熵(ApEn),将其应用于脑电图(EEG)信号,研究神经对室内环境中温度和光线变化的反应。通过战略性地将电极放置在与温度和光线处理相关的特定脑区,我们展示了 ApEn 如何受到室内因素的影响。我们还整合了来自多传感器手环的心脏指数,创建了温度条件机器学习分类器。结果显示,在额叶前部和颞顶叶区域,中性温度条件会产生较高的 ApEn 值。前额叶区域的 ApEn 值呈现出从中性到温暖条件逐渐降低的趋势,而寒冷则处于中间位置。只有在颞顶区,光线和部位因素之间存在明显的交互作用。与蓝光和红光条件相比,中性光条件下的 ApEn 值更高。前额叶 ApEn 与热舒适度得分之间的正相关表明,熵与感知热舒适度之间存在联系。我们的二次 SVM 分类器结合了熵和心脏特征,在对温度感觉进行分类时表现出很强的性能(在 AUC、准确性、灵敏度和特异性方面达到 90%)。这项研究深入揭示了神经对室内因素的反应,并提出了一种利用脑电图熵和心脏特征进行温度分类的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals.

Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals.

Understanding the neural responses to indoor characteristics like temperature and light is crucial for comprehending how the physical environment influences the human brain. Our study introduces an innovative approach using entropy analysis, specifically, approximate entropy (ApEn), applied to electroencephalographic (EEG) signals to investigate neural responses to temperature and light variations in indoor environments. By strategically placing electrodes over specific brain regions linked to temperature and light processing, we show how ApEn can be influenced by indoor factors. We also integrate heart indices from a multi-sensor bracelet to create a machine learning classifier for temperature conditions. Results showed that in anterior frontal and temporoparietal areas, neutral temperature conditions yield higher ApEn values. The anterior frontal area showed a trend of gradually decreasing ApEn values from neutral to warm conditions, with cold being in an intermediate position. There was a significant interaction between light and site factors, only evident in the temporoparietal region. Here, the neutral light condition had higher ApEn values compared to blue and red light conditions. Positive correlations between anterior frontal ApEn and thermal comfort scores suggest a link between entropy and perceived thermal comfort. Our quadratic SVM classifier, incorporating entropy and heart features, demonstrates strong performance (until 90% in terms of AUC, accuracy, sensitivity, and specificity) in classifying temperature sensations. This study offers insights into neural responses to indoor factors and presents a novel approach for temperature classification using EEG entropy and heart features.

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来源期刊
CiteScore
8.80
自引率
2.20%
发文量
121
审稿时长
4-8 weeks
期刊介绍: Pflügers Archiv European Journal of Physiology publishes those results of original research that are seen as advancing the physiological sciences, especially those providing mechanistic insights into physiological functions at the molecular and cellular level, and clearly conveying a physiological message. Submissions are encouraged that deal with the evaluation of molecular and cellular mechanisms of disease, ideally resulting in translational research. Purely descriptive papers covering applied physiology or clinical papers will be excluded. Papers on methodological topics will be considered if they contribute to the development of novel tools for further investigation of (patho)physiological mechanisms.
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