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Automated recognition of hypertension through overnight continuous HRV monitoring

  • Hongbo Ni
  • Sunyoung Cho
  • Jennifer Mankoff
  • Jun Yang
  • Anind k. Dey
Original Research

Abstract

Hypertension is a common and chronic disease, caused by high blood pressure. Since hypertension often has no warning signs or symptoms, many cases remain undiagnosed. Untreated or sub-optimally controlled hypertension may lead to cardiovascular, cerebrovascular and renal morbidity and mortality, along with dysfunction of the autonomic nervous system. Therefore, it could be quite valuable to predict or provide early warnings about hypertension. Heart rate variability (HRV) analysis has emerged as the most valuable non-invasive test to assess autonomic nervous system function, and has great potential for detecting hypertension. However, HRV indicators may be subtle and present at random, resulting in two challenges: how to support continuous monitoring for hours at a time while being unobtrusive, and how to efficiently analyze the collected data to minimize data collection and user burden. In this paper, we present a machine learning-based approach for detecting hypertension, using a waist belt continuous sensing system that is worn overnight. Using 24 hypertension patients and 24 healthy controls, we demonstrate that our approach can differentiate hypertension patients from healthy controls with 93.33% accuracy. This represents a promising approach for performing hypertension classification in the field, and also we would improve its performance based on a large number of hypertensive subjects monitored by the proposed pervasive sensors.

Keywords

Human-centered computing Ubiquitous computing Computing methodologies Machine learning Electrocardiogram Pyramid methods Healthcare Heart rate sensing 

Notes

Acknowledgements

We thank the reviewers for the valuable comments and for the time spent towards the improvement of the paper. This work was supported by the China Scholarship Council, and is supported by the Key Project of National Found of Science of China (61332013) and Fundamental Research Grant of NWPU (3102015JSJ0010).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Hongbo Ni
    • 1
  • Sunyoung Cho
    • 2
  • Jennifer Mankoff
    • 2
  • Jun Yang
    • 3
  • Anind k. Dey
    • 2
  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.Beijing Aviation Medical InstituteBeijingChina

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