Centre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Sensors (Basel). 2022 Aug 30;22(17):6528. doi: 10.3390/s22176528.
Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements' reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland-Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.
超短期 HRV 特征可评估微小的自主神经系统变化,例如在高要求任务中认知压力峰值引起的变化。多项研究将超短期和短期 HRV 测量进行比较,以研究其可靠性。然而,现有的实验是在认知要求较低的环境中进行的。在本文中,我们提出在认知要求高的任务下使用近乎真实的环境来评估这些测量的可靠性。为此,我们选择了 31 个 HRV 特征,从 21 名执行代码理解任务的程序员采集的数据中提取出来,并在 18 个不同的时间框架内进行了比较,时间框架从 3 分钟到 10 秒不等。在使用较大的窗口(3 分钟)提取的特征和使用其他 17 个时间框架提取的相同特征之间,进行了统计显著性和相关性测试。我们将这些分析与 Bland-Altman 图进行了配对,以检查提取窗口大小如何影响 HRV 特征。主要结果表明,当使用 60 秒窗口时,有 13 个特征的相关性至少达到 50%。HF 和 mNN 特征在使用 30 秒窗口时的相关性约为 50%。30 秒窗口是被认为具有可靠测量结果的最小时间框架。此外,mNN 特征证明对时间分辨率的缩短具有很强的鲁棒性。