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Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study

  • Mobile & Wireless Health
  • Published:
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Abstract

Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. We show that early disease prediction is possible through collecting one’s PPG-based heart rate information.

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References

  1. Jeyhani, Vala, et al. "Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals." 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015.

  2. Allen, J., Photoplethysmography and its application in clinical physiological measurement. Physiological measurement 28.3:R1–R39, 2007.

    Article  Google Scholar 

  3. Melillo, P. et al., Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PloS one 10.3:e0118504, 2015.

    Article  CAS  Google Scholar 

  4. Zhang, Z., Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction. IEEE Transactions on Biomedical Engineering 62.8:1902–1910, 2015.

    Article  Google Scholar 

  5. Adidas. Adidas miCoach, "real time voice coaching and zone based cardio training". Available from: http://micoach.adidas.com/.

  6. Mio. "Continuous heart rate monitoring technology without a chest strap". Available from: http://www.mioglobal.com/mio-continuous-heart-rate-technology.htm.

  7. Nellcor. "Nellcor N-65 Pluse Oximetry Monitor". Available from: http://www.covidien.com/rms/products/pulse-oximetry/nellcor-n65-pulse-oximetry-monitor#features.

  8. Gacek, Adam, and Witold Pedrycz, eds. ECG signal processing, classification and interpretation: A comprehensive framework of computational intelligence. Springer Science & Business Media, 2011.

  9. Peel III, Harry H. "System and method for accurately monitoring the cardiovascular state of a living subject." U.S. Patent No. 5,931,790. 3 Aug. 1999.

  10. Bruser, C. et al., Adaptive beat-to-beat heart rate estimation in ballistocardiograms. IEEE Transactions on Information Technology in Biomedicine 15.5:778–786, 2011.

    Article  Google Scholar 

  11. Selvaraj, N. et al., Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of medical engineering & technology 32(6):479–484, 2008.

    Article  CAS  Google Scholar 

  12. Arumugam, J. M. R., and Mary, A., Heart rate variability a cardiac indicator in diabetic autonomic neuropathy: A systematic review. International Journal of Medical Research 1.2:13–16, 2013.

    Article  Google Scholar 

  13. Patrick, T. H. H. et al., Cardiovascular diagnostics mining on Borneo island: From labour-intensive to prototype. Informatics for Health and Social Care 34.1(1):–9, 2009.

  14. Mussalo, H. et al., Heart rate variability and its determinants in patients with severe or mild essential hypertension. Clinical Physiology 21.5:594–604, 2001.

    Article  Google Scholar 

  15. Raghunath, M. T., and Narayanaswami, C., User interfaces for applications on a wrist watch. Personal and Ubiquitous Computing 6.1:17–30, 2002.

    Article  Google Scholar 

  16. Pandian, P. S. et al., Smart Vest: Wearable multi-parameter remote physiological monitoring system. Medical engineering & physics 30.4:466–477, 2008.

    Article  Google Scholar 

  17. Gertsch, Jeffrey H., et al. "Electronic helmet." U.S. Patent No. 8,001,623. 23 Aug. 2011.

  18. Google. "Google Glasses Development Kit." Available from: https://developers.google.com/glass/.

  19. Poh, M.-Z. et al., Cardiovascular monitoring using earphones and a mobile device. IEEE Pervasive Computing (4.11):18–26, 2012.

  20. Rhee, S., Yang, B.-H., and Asada, H. H., Artifact-resistant power-efficient design of finger-ring plethysmographic sensors. IEEE Transactions on Biomedical Engineering 48(7):795–805, 2001.

    Article  PubMed  CAS  Google Scholar 

  21. Warren, K. M. et al., Improving pulse rate measurements during random motion using a wearable multichannel reflectance Photoplethysmograph. Sensors 16.3:342, 2016.

    Article  Google Scholar 

  22. Merilahti, J. et al., Compliance and technical feasibility of long-term health monitoring with wearable and ambient technologies. Journal of telemedicine and telecare 15.6:302–309, 2009.

    Article  Google Scholar 

  23. Mazzù, M. et al., Wireless-accessible sensor populations for monitoring biological variables. Journal of telemedicine and telecare 14.3:135–137, 2008.

    Article  Google Scholar 

  24. Chen, W. et al., Design of an integrated sensor platform for vital sign monitoring of newborn infants at neonatal intensive care units. Journal of Healthcare Engineering 1.4:535–554, 2010.

    Article  Google Scholar 

  25. Alexander, G. et al., Evolution of an early illness warning system to monitor frail elders in independent living. Journal of healthcare engineering 2.3:337–364, 2011.

    Article  Google Scholar 

  26. Huang, J.-H. et al., Implementation of a Wireless Sensor Network for Heart Rate Monitoring in a Senior Center. Telemedicine and e-Health 21.6:493–498, 2015.

    Article  Google Scholar 

  27. Alliance, ZigBee. "Zigbee specification." (2006).

  28. MILL package for MATLAB. Available from: http://www.cs.cmu.edu/~juny/MILL/.

  29. Melillo, P. et al., Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE journal of biomedical and health informatics 17.3:727–733, 2013.

    Article  Google Scholar 

  30. Melillo, P. et al., Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Medical & biological engineering & computing 49(1):67–74, 2011.

    Article  Google Scholar 

  31. Landolina, M. et al., Heart rate variability monitored by the implanted device predicts response to CRT and long-term clinical outcome in patients with advanced heart failure. European journal of heart failure 10.11:1073–1079, 2008.

    Article  Google Scholar 

  32. Kristiansen, J. et al., Comparison of two systems for long-term heart rate variability monitoring in free-living conditions-a pilot study. Biomedical engineering online 10.1(1), 2011.

  33. Yao, Q. et al., Design and development of a medical big data processing system based on hadoop. Journal of medical systems 39.3:1–11, 2015.

    Google Scholar 

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Authors and Affiliations

  1. School of Chinese Medicine, China Medical University, Taichung, Taiwan

    Kun-chan Lan

  2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Kun-chan Lan & Paweeya Raknim

  3. Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan

    Wei-Fong Kao

  4. Sport Information and Communication Department, National Taiwan University of Sport, Taichung, Taiwan

    Jyh-How Huang

Authors
  1. Kun-chan Lan
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  2. Paweeya Raknim
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  3. Wei-Fong Kao
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  4. Jyh-How Huang
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Corresponding author

Correspondence to Jyh-How Huang.

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This article is part of the Topical Collection on Mobile & Wireless Health

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Lan, Kc., Raknim, P., Kao, WF. et al. Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study. J Med Syst 42, 103 (2018). https://doi.org/10.1007/s10916-018-0942-5

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  • DOI: https://doi.org/10.1007/s10916-018-0942-5

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