Physiological studies have found that the autonomic nervous system plays an important role in controlling blood pressure values. This paper, based on machine learning approaches, analysed short-term heart rate variability to determine differences in autonomic nervous function between hypertensive patients and normal population. The electrocardiogram (ECG) of hypertensive patients are 137 ECG recordings provided by Smart Health for Assessing the Risk of Events via ECG (SHAREE database). The RR intervals of healthy subjects include the data of 18 subjects from the MIT-BIH Normal Sinus Rhythm Database (nsrdb) and 54 subjects from the Normal Sinus Rhythm RR Interval Database (nsr2db). In this paper, each RR segment includes continuous 500 beats. Seventeen features were extracted to distinguish the hypertensive heart beat rhythms from the normal ones, and Kolmogorov-Smirnov test and sequential backward selection (SBS) were applied to get the best feature combinations. In addition, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) were applied as classifiers in the study. The performance of each classifier was evaluated independently using the leave-one-subject-out validation method. The best predictive model was based on RF and enabled to identify hypertensive patients by five features with an accuracy of 86.44%. The best five HRV features are sample entropy (SampEn), very low frequency spectral powers (VLF), root mean square of successful differences (RMSSD), ratio of low frequency spectral powers and high frequency spectral powers (LF/HF) and vector angle index (VAI). The results of the study show sympathetic overactivity and decreased parasympathetic tone in hypertensive patients.

中文翻译：

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基于短期心率变异性的高血压患者自主神经模式分析

生理研究发现，自主神经系统在控制血压值方面起着重要作用。本文基于机器学习方法，分析了短期心率变异性，以确定高血压患者与正常人群之间自主神经功能的差异。高血压患者的心电图（ECG）是Smart Health提供的137个ECG记录，用于通过ECG（SHAREE数据库）评估事件的风险。健康受试者的RR间隔包括来自MIT-BIH正常窦性心律节奏数据库（nsrdb）的18个受试者和来自正常窦性心律RR间隔​​数据库（nsr2db）的54个受试者的数据。在本文中，每个RR片段都包含连续的500个拍子。提取了17个特征以区分高血压心律和正常心律，并使用Kolmogorov-Smirnov测试和顺序向后选择（SBS）来获得最佳特征组合。此外，支持向量机（SVM），k最近邻（KNN）和随机森林（RF）被用作研究中的分类器。每个分类器的性能都是使用留一法验证方法独立评估的。最佳的预测模型是基于RF的，能够通过五种特征识别高血压患者，准确度为86.44％。最佳的五个HRV功能是样本熵（SampEn），极低频谱功率（V​​LF），成功差的均方根（RMSSD），低频频谱功率与高频频谱功率的比率（LF / HF）和矢量角指数（VAI）。