Skip to main content

Advertisement

SpringerLink
Log in

Automated recognition of hypertension through overnight continuous HRV monitoring

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS (2006) Heart rate variability: a review. Med Biol Eng Comput 44(12):1031–1051. doi:10.1007/978-3-540-36675-1_5

    Article  Google Scholar 

  • Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69–87

    Article  Google Scholar 

  • EH Adelson, EP Simoncelli, WT Freeman (2003) Pyramids and multiscale representations. In: Proceedings of ECVP

  • BO Al-Tabbaa, RJ Oweis (2014) QRS detection and heart rate variability analysis: a survey. Biomed Sci Eng 2(1):13–34

    Google Scholar 

  • Awal A, Mostafa SS, Ahmed M (2011) Performance analysis of savitzky-golay smoothing filter using ECG signal. Int J Comput Inf Technol 1(2):24–29

    Google Scholar 

  • HJ Baek, JS Kim, KK Kim, KS Park (2008) System for unconstrained ECG measurement on a toilet seat using capacitive coupled electrodes: the efficacy and practicality. In: Proceedings of EMBS, pp 2326–2328

  • Y-L Boureau, J Ponce, Y Lecun (2010) A theoretical analysis of feature pooling in visual recognition. In: Proceedings of ICML, pp 111–118

  • Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540. doi:10.1515/9781400827268.28

    Article  Google Scholar 

  • H-C Chou, Y-M Wang, H-Y Chang (2015) Design intelligent wheelchair with ECG measurement and wireless transmission function. Technol Health Care 24:S345–S355. doi:10.3233/THC-151092

    Article  Google Scholar 

  • RR Coifman, Y Meyer, S Quake, MV Wickerhauser (1993) Signal processing and compression with wavelet packets. Wavelets Appl 442:363–379. doi:10.1007/978-94-011-1028-0_18

    Article  MATH  Google Scholar 

  • Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 89(6):068102. doi:10.1103/PhysRevLett.89.068102

    Article  Google Scholar 

  • Coyle S, Lau K-T, Moyna N, Gorman DO, Diamond D Fabio Di F, D Costanzo, P Salvo, MG Trivella, DE De Rossi (2010) BIOTEX-Biosensing textiles for personalised healthcare management. IEEE Trans Inf Technol Biomed 14(2):364–370. doi:10.1109/TITB.2009.2038484

    Article  Google Scholar 

  • Curone D, Secco EL, Tognetti A, Loriga G, Dudnik G, Risatti M, Whyte R, Bonfiglio A, Magenes G (2010) Smart garments for emergency operators: the ProeTEX project. IEEE Trans Inf Technol Biomed 14(3):694–701. doi:10.1109/TITB.2010.2045003

    Article  Google Scholar 

  • Da He D, Winokur ES, Sodini CG (2015) An Ear-Worn Vital Signs Monitor. IEEE Trans Biomed Eng 62(11):2547–2552

    Article  Google Scholar 

  • D Farotto, L Atallah, P van der Heijden, L Grieten (2015) ECG synthesis from separate wearable bipolar electrodes. In: Proceedings of EMBC, pp 5058–5061. doi:10.1109/EMBC.2015.7319528

  • Feng XL, Pang M, Beard J (2014) Health system strengthening and hypertension awareness, treatment and control: data from the China Health and Retirement Longitudinal Study. Bull World Health Organ 92(1):29–41. doi:10.2471/BLT.13.124495

    Article  Google Scholar 

  • Ho Y-L, Lin C, Lin Y-H, Lo M-T (2011) The prognostic value of non-linear analysis of heart rate variability in patients with congestive heart failure at a pilot study of multiscale entropy. PLoS One 6(4):e18699. doi:10.1371/journal.pone.0018699

    Article  Google Scholar 

  • K Ito, Y Fukuoka, G Cauwenberghs, A Ueno (2013) Noncontact sensing of electrocardiographic potential and body proximity by in-bed conductive fabrics. In: Proceedings of CinC, pp 523–526.

  • KK Kim, YK Lim, KS Park (2006) Common mode noise cancellation for electrically non-contact ECG measurement system on a chair. In: Proceedings of EMBS, pp 5881–5883

  • Kwon S, Kang S, Lee Y, Yoo C, Park K (2014) Unobtrusive monitoring of ECG-derived features during daily smartphone use. In: Proceedings of EMBC. IEEE, pp 4964–4967

  • Lake DE, Richman JS, Griffin MP, Moorman JR (2002) Sample entropy analysis of neonatal heart rate variability. Am J Physiol 238(3):789–797. doi:10.1152/ajpregu.00069.2002

    Article  Google Scholar 

  • HJ Lee, SH Hwang, HN Yoon, WK Lee, KS Park (2015) Heart rate variability monitoring during sleep based on capacitively coupled textile electrodes on a bed. Sensors 15(5):11295–11311. doi:10.3390/s150511295

    Article  Google Scholar 

  • S Lemieux, Y Bengio, D Eck (2011) Temporal pooling and multiscale learning for automatic annotation and ranking of music audio. In: Proceedings of ISMIR, pp 729–734

  • Lewicke A, Sazonov E, Corwin MJ, Neuman M, Schuckers S (2008) and CHIME Study Group. Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models. IEEE Trans Biomed Eng 55(1):108–118. doi:10.1109/TBME.2007.900558

    Article  Google Scholar 

  • Li W, Gu H, Teo KK, Bo J, Wang Y, Yang J, Wang X, Zhang H, Sun Y, Jia X (2016) and others. Hypertension prevalence, awareness, treatment, and control in 115 rural and urban communities involving 47000 people from China. J Hypertens 34(1):39–46

    Article  Google Scholar 

  • Lim YG, Kim KK, K. S (2007) Park.ECG recording on a bed during sleep without direct skin-contact. Biomed Eng IEEE Trans 54(4):718–725

    Article  Google Scholar 

  • X Long, P Fonseca, R Haakma, RM Aarts, J Foussier (2012) Time-frequency analysis of heart rate variability for sleep and wake classification. In: Proceedings of BIBE, pp 85–90. doi:10.1109/BIBE.2012.6399712

  • D Lowe (2004) Distinctive image features from scale-invariant keypoints. IJCV 60(4):91–110. doi:10.1023/B:VISI.0000029664.99615.94

    Article  MathSciNet  Google Scholar 

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. doi:10.1515/9781400827268.494

    Article  MATH  Google Scholar 

  • G Manis, A Alexandridi, S Nikolopoulos, K Davos (2005) The effect of white noise and false peak Detection on HRV Analysis. In: Proceedings of ICINCO, pp 161–166

  • Melillo P, lzzo R, Orrico A, Scala P, Attanasio M, Mirra M, Luca ND, Pecchia L (2015) Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS One 10(3):e0118504. doi:10.1371/journal.pone.0118504

    Article  Google Scholar 

  • Merritt CR, Nagle HT, Grant E (2009) Fabric-based active electrode design and fabrication for health monitoring clothing. IEEE Trans Inf Technol Biomed 13(2):274–280. doi:10.1109/TITB.2009.2012408

    Article  Google Scholar 

  • Natarajan N, Balakrishnan AK, kkirapandian K (2014) A study on analysis of heart rate variability in hypertensive individuals. Int J Biomedical Adv Res 5(2):109–111. doi:10.7439/ijbar.v5i2.659

    Article  Google Scholar 

  • J Nikolic-Popovic, R Goubran (2013) Impact of motion artifacts on Heart Rate Variability measurements and classification performance. In: MeMeA, pp 156–159. doi:10.1109/MeMeA.2013.6549726

  • J Nikolic-Popovic, R Goubran (2014) Towards increased usability of noisy ECG signals in HRV-based classifiers. In: MeMeA, pp 1–4. Doi:10.1109/MeMeA.2014.6860125

  • Pincus SM (1991) Approximate entropy as a measure of system complexity. PNAS 88(6):2297–2301. doi:10.1073/pnas.88.6.2297

    Article  MathSciNet  MATH  Google Scholar 

  • MG Poddar, V Kumar, Y Paul Sharma (2014) Heart rate variability based classification of normal and hypertension cases by linear-nonlinear method. Def Sci J 64(6):542–548

    Article  Google Scholar 

  • Puente ET (2010) Heart rate variability analysis during normal and hypertensive pregnancy. Doctoral dissertation, Faculty of Pharmacy, University of Porto

  • Ramirez-Villegas JF, Lam-Espinosa E, Ramirez-Moreno DF, Calvo-Echeverry PC, Agredo-Rodriguez W (2011) Heart rate variability dynamics for the prognosis of cardiovascular risk. PLoS one 6(2):e17060

    Article  Google Scholar 

  • Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, Hailpern SM, Ho M, Howard V, Kissela B (2008) American Heart Association Statistics Committee, and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 117(4):e25–e146

    Google Scholar 

  • J Rubin, H Eldardiry, R Abreu, S Ahern, H Du, A Pattekar, DG Bobrow (2015) Towards a mobile and wearable system for predicting panic attacks. In: Proceedings of UbiComp, pp 529–533. doi:10.1145/2750858.2805834

  • Savitzky A, MJE Golay (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639. doi:10.1021/ac60214a047

    Article  Google Scholar 

  • Schroeder EB, Liao D, Chambless LE, Prineas RJ, Evans GW, Heiss G (2003) Hypertension, blood pressure, and heart rate variability the atherosclerosis risk. In communities (ARIC) study. Hypertension 42(6):1106–1111

    Article  Google Scholar 

  • P Shi, H-L Yu (2013) Heart rate variability in essential hypertension patients with different stages by nonlinear analysis: a preliminary study. Adv Biomed Eng Res 1(3):33–39

    Google Scholar 

  • M Signorini, M Ferrario, M Marchetti, A Marseglia (2006) Nonlinear analysis of heart rate variability signal for the characterization of cardiac heart failure patients. In Proceedings of EMBS, pp 3431–3434. doi:10.1109/IEMBS.2006.259744

  • Singh JP, Larson MG, Tsuji H, Evans JC, O’Donnell CJ, Levy D (1998) Reduced heart rate variability and new-onset hypertension- Insights into pathogenesis of hypertension: the Framingham heart study. J Hum Hypertens 32(2):293–297. doi:10.1161/01.HYP.32.2.293

    Article  Google Scholar 

  • Subasi A (2005) Epileptic seizure detection using dynamic wavelet network. Expert Syst Appl 29(2):343–355. doi:10.1016/j.eswa.2005.04.007

    Article  Google Scholar 

  • F-T Sun, C Kuo, H-T Cheng, S Buthpitiya, P Collins, M Griss (2012) Activity-aware mental stress detection using physiological sensors. Mob Comput Appl Serv 76:211–230. doi:10.1007/978-3-642-29336-8_12

    Article  Google Scholar 

  • Terathongkum S, Pickler RH (2004) Relationships among heart rate variability, hypertension, and relaxation techniques. J Vasc Nurs 22(3):78–82. doi:10.1016/j.jvn.2004.06.003

    Article  Google Scholar 

  • Virtanen R, Jula A, Kuusela T, Helenius H, L-M Voipio-Pulkki (2003)Reduced heart rate variability in hypertension: associations with lifestyle factors and plasma renin activity. J Hum Hypertens 17(3):171–179. doi:10.1038/sj.jhh.1001529

    Article  Google Scholar 

  • Wall HK, Hannan JA, Wright JS (2014) Patients with undiagnosed hypertension: hiding in plain sight. JAMA 312(19):1973. doi:10.1001/jama.2014.15388

    Article  Google Scholar 

  • World Health Organization (WHO and others) (2015) A global brief on hypertension: silent killer, global public health crisis. World

  • K-F Wu, Y-T Zhang (2008) Contactless and continuous monitoring of heart electric activities through clothes on a sleeping bed. In: Proceedings of ITAB, pp 282–285. doi:10.1109/ITAB.2008.4570586

Download references

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).

Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Hongbo Ni

  2. Carnegie Mellon University, Pittsburgh, USA

    Sunyoung Cho, Jennifer Mankoff & Anind k. Dey

  3. Beijing Aviation Medical Institute, Beijing, China

    Jun Yang

Authors
  1. Hongbo Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunyoung Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jennifer Mankoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anind k. Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongbo Ni.

Additional information

Permission to make digital or hardcopies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credits permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. © 2010 ACM 1539–9087/2010/03-ART39 $15.00. doi:10.1145/0000000.0000000.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ni, H., Cho, S., Mankoff, J. et al. Automated recognition of hypertension through overnight continuous HRV monitoring. J Ambient Intell Human Comput 9, 2011–2023 (2018). https://doi.org/10.1007/s12652-017-0471-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-017-0471-y

Keywords

Search

Navigation