PDF(Pubmed)
Abstract：
Heart rate variability (HRV) parameters can reveal the performance of the autonomic nervous system and possibly estimate the type of its malfunction, such as that of detecting the blood glucose level. Therefore, we aim to find the impact of other factors on the proper calculation of HRV. In this paper, we research the relation between HRV and the age and gender of the patient to adjust the threshold correspondingly to the noninvasive glucose estimator that we are developing and improve its performance. While most of the literature research so far addresses healthy patients and only short- or long-term HRV, we apply a more holistic approach by including both healthy patients and patients with arrhythmia and different lengths of HRV measurements (short, middle, and long). The methods necessary to determine the correlation are (i) point biserial correlation, (ii) Pearson correlation, and (iii) Spearman rank correlation. We developed a mathematical model of a linear or monotonic dependence function and a machine learning and deep learning model, building a classification detector and level estimator. We used electrocardiogram (ECG) data from 4 different datasets consisting of 284 subjects. Age and gender influence HRV with a moderate correlation value of 0.58. This work elucidates the intricate interplay between individual input and output parameters compared with previous efforts, where correlations were found between HRV and blood glucose levels using deep learning techniques. It can successfully detect the influence of each input.
摘要：
心率变异性（HRV）参数可以揭示自主神经系统的性能，并可能估计其故障的类型，例如检测血糖水平。因此,我们的目标是找到其他因素对正确计算HRV的影响。在本文中,我们研究了HRV与患者年龄和性别之间的关系,以根据我们正在开发的无创血糖估算器相应地调整阈值并改善其性能.虽然到目前为止，大多数文献研究都针对健康患者，只有短期或长期的HRV，我们采用更全面的方法，包括健康患者和心律失常患者以及不同长度的HRV测量(短，中间,和长)。确定相关性所需的方法是(I)点双材料相关性，(二)皮尔逊相关性，和(iii)Spearman等级相关。我们开发了线性或单调依赖函数的数学模型以及机器学习和深度学习模型，构建分类检测器和水平估计器。我们使用了来自由284名受试者组成的4个不同数据集的心电图(ECG)数据。年龄和性别影响HRV，中度相关值为0.58。与以前的工作相比，这项工作阐明了单个输入和输出参数之间的复杂相互作用，其中使用深度学习技术发现HRV和血糖水平之间存在相关性。它可以成功地检测每个输入的影响。
