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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.<\/jats:p>","DOI":"10.1515\/bmt-2019-0184","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T07:25:21Z","timestamp":1597735521000},"page":"71-79","source":"Crossref","is-referenced-by-count":6,"title":["Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability"],"prefix":"10.1515","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3800-9065","authenticated-orcid":false,"given":"Ruiqi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Southwest University , Chongqing , China"},{"name":"and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing , Chongqing , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengchun","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Southwest University , Chongqing , China"},{"name":"and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing , Chongqing , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Southwest University , Chongqing , China"},{"name":"and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing , Chongqing , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Southwest University , Chongqing , China"},{"name":"and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing , Chongqing , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanhui","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Southwest University , Chongqing , China"},{"name":"and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing , Chongqing , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"key":"2021022511301860058_j_bmt-2019-0184_ref_001_w2aab3b7c46b1b6b1ab2b2b1Aa","doi-asserted-by":"crossref","unstructured":"Lu, J, Lu, Y, Wang, X, Li, X, Linderman, GC, Wu, C, et al. 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