Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability

Ruiqi Zhang; Zhengchun Hua; Chen Chen; Guangyuan Liu; Wanhui Wen

doi:10.1515/bmt-2019-0184

Article

Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability

Ruiqi Zhang , Zhengchun Hua , Chen Chen , Guangyuan Liu and Wanhui Wen

Published/Copyright: August 10, 2020

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From the journal Biomedical Engineering / Biomedizinische Technik Volume 66 Issue 1

Abstract

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.

Keywords: autonomic nervous system; hypertension; machine learning; short-term HRV analysis

Corresponding authors: Guangyuan Liu, School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China, E-mail: liugy@swu.edu.cn; and Wanhui Wen, School of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing 400715, China, E-mail: cwenwanh@swu.edu.cn

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 61103132, 61872301

Funding source: Fundamental Research Funds for the Central Universities of China

Award Identifier / Grant number: XDJK2013A020

Acknowledgments

The authors would like to thank all the participants who took part in the experiments.

Funding: This research was funded by the National Science Foundation of China (Grant No. 61103132 and No. 61872301) and the Fundamental Research Funds for the Central Universities of China (Grant No. XDJK2013A020).
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interest: The authors declare no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: Not applicable.
Availability of data and material declaration: All ECG data comes from public databases, which were downloaded from the physionet.org website.

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Received: 2019-07-18

Accepted: 2020-06-30

Published Online: 2020-08-10

Published in Print: 2021-02-23

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Articles in the same Issue

https://doi.org/10.1515/bmt-2019-0184

Keywords for this article

autonomic nervous system; hypertension; machine learning; short-term HRV analysis