Department of Biomedical, Electronic and Telecommunication Engineering, University of Naples "Federico II", Via Claudio 21, Naples, Italy.
Biomed Eng Online. 2011 Nov 7;10:96. doi: 10.1186/1475-925X-10-96.
This study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection.
42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA).
Almost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively.
The results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination.
本研究旨在探讨心率变异性（HRV）在真实生活应激源下的变化，并提出一种基于 HRV 非线性特征的分类器，用于自动应激检测。
42 名学生自愿参与 HRV 和应激研究。每位学生进行两次记录：一次是在正在进行的大学考试期间，假设为真实生活应激源，另一次是在假期后。通过使用 Poincaré 图、近似熵、关联维数、去趋势波动分析和递归图对 HRV 的非线性分析。为了进行统计比较，我们采用了 Wilcoxon 符号秩检验，为了开发分类器，我们采用了线性判别分析（LDA）。
几乎所有测量心率复杂性的 HRV 特征在应激阶段都显著降低。LDA 生成了一个简单的分类器，基于两个 Poincaré 图参数和近似熵，能够以总分类准确率、敏感度和特异性分别为 90%、86%和 95%的准确率进行应激检测。
本研究结果表明，使用短期 ECG 记录进行 HRV 的非线性分析可能是一种有效的自动检测真实生活应激条件的方法，如大学考试。
