Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography | SpringerLink
Skip to main content
Springer Nature Link
Log in

Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12534))

Included in the following conference series:

Abstract

Pulse transit time (PTT) provides a cuffless method to measure and predict blood pressure, which is essential in long term cardiac activity monitoring. Photoplethysmography (PPG) sensors provide a low-cost and wearable approach to obtain PTT measurements. The current approach to calculating PTT relies on quasi-periodic pulse event extractions based on PPG local signal characteristics. However, due to inherent noise in PPG, especially at uncontrolled settings, this approach leads to significant errors and even missing potential pulse events. In this paper, we propose a novel approach where global features (all samples) of the time-series data are used to develop a machine learning model to extract local pulse events. Specifically, we contribute 1) a new noise resilient machine learning model to extract events from PPG and 2) results from a study showing accuracy over state of the art (e.g. HeartPy) and 3) we show that MLPTT outperforms HeartPy peak detection, especially for noisy photoplethysmography data.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Geddes, L.A., Voelz, M.H., Babbs, C.F., Bourland, J.D., Tacker, W.A.: Pulse transit time as an indicator of arterial blood pressure. Psychophysiology 18(1), 71–74 (1981). https://doi.org/10.1111/j.1469-8986.1981.tb01545.x. ISSN 0048-5772

    Article  Google Scholar 

  2. Poon, C.C.Y., Zhang, Y.T.: Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 5877–5880 (2005). https://doi.org/10.1109/IEMBS.2005.1615827

  3. Lane, J.D., Greenstadt, L., Shapiro, D., Rubinstein, E.: Pulse transit time and blood pressure: an intensive analysis. Psychophysiology 20(1), 45–49 (1983). https://doi.org/10.1111/j.1469-8986.1983.tb00899.x. ISSN 0048-5772

    Article  Google Scholar 

  4. Pitson, D.J., Stradling, J.R.: Value of beat-to-beat blood pressure changes, detected by pulse transit time, in the management of the obstructive sleep apnoea/hypopnoea syndrome. Eur. Respir. J. 12(3), 685–692 (1998)

    Article  Google Scholar 

  5. Gesche, H., Grosskurth, D., Küchler, G., Patzak, A.: Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. Eur. J. Appl. Physiol. 112, 309–315 (2011). https://doi.org/10.1007/s00421-011-1983-3

    Article  Google Scholar 

  6. Ripoll, V.R., Vellido, A.: Blood pressure assessment with differential pulse transit time and deep learning: a proof of concept. Kidney Dis. 5(1), 23–27 (2019). https://doi.org/10.1159/000493478

    Article  Google Scholar 

  7. Sklarsky, A., Garvin, N.M., Pawelczyk, J.A.: Limitations of pulse transit time to estimate blood pressure. FASEB J. 33(1 supplement), 562.13 (2019). https://doi.org/10.1096/fasebj.2019.33.1_supplement.562.13

    Article  Google Scholar 

  8. Mukkamala, R., et al.: Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE Trans. Biomed. Eng. 62(8), 1879–1901 (2015). https://doi.org/10.1109/tbme.2015.2441951. ISSN 0018-9294

    Article  Google Scholar 

  9. Smith, R.P., Argod, J., Pépin, J.-L., Lévy, P.A.: Pulse transit time: an appraisal of potential clinical applications. Thorax 54(5), 452–457 (1999). https://doi.org/10.1136/thx.54.5.452

    Article  Google Scholar 

  10. Sharwood-Smith, G., Bruce, J., Drummond, G.: Assessment of pulse transit time to indicate cardiovascular changes during obstetric spinal anaesthesia. BJA Br. J. Anaesth. 96(1), 100–105 (2005). https://doi.org/10.1093/bja/aei266. ISSN 0007-0912

    Article  Google Scholar 

  11. Obrist, P.A., Light, K.C., McCubbin, J.A., Hutcheson, J.S., Hoffer, J.L.: Pulse transit time: relationship to blood pressure and myocardial performance. Psychophysiology 16(3), 292–301 (1979). https://doi.org/10.1111/j.1469-8986.1979.tb02993.x. ISSN 0048-5772

    Article  Google Scholar 

  12. Pèpin, J.-L., et al.: Pulse transit time improves detection of sleep respiratory events and microarousals in children. CHEST 127(3), 722–730 (2005). https://doi.org/10.1378/chest.127.3.722. ISSN 0012-3692

    Article  Google Scholar 

  13. García, M.T.G., et al.: Can pulse transit time be useful for detecting hypertension in patients in a sleep unit? Archivos de Bronconeumología (English Ed.) 50(7), 278–284 (2014). https://doi.org/10.1016/j.arbr.2014.05.001. ISSN 15792129

    Article  Google Scholar 

  14. Elgendi, M., et al.: The use of photoplethysmography for assessing hypertension. NPJ Digital Med. 2(1), 60 (2019). https://doi.org/10.1038/s41746-019-0136-7. ISSN 2398-6352

    Article  Google Scholar 

  15. Nabeel, P.M., Joseph, J., Sivaprakasam, M.: Arterial compliance probe for local blood pulse wave velocity measurement, vol. 2015 (2015). https://doi.org/10.1109/EMBC.2015.7319689

  16. Samaniego, N.C., Morris, F., Brady, W.J.: Electrocardiographic artefact mimicking arrhythmic change on the ECG. Emerg. Med. J. 20(4), 356–357 (2003). https://doi.org/10.1136/emj.20.4.356

    Article  Google Scholar 

  17. Anton, O., Fernandez, R., Rendon-Morales, E., Aviles-EspinosaÂ, R., Jordan, H., Rabe, H.: Heart rate monitoring in newborn babies: a systematic review. Neonatology 116(3) (2019). https://doi.org/10.1159/000499675

  18. Pollreisz, D., TaheriNejad, N.: Detection and removal of motion artifacts in PPG signals. Mob. Netw. Appl (2019). https://doi.org/10.1007/s11036-019-01323-6. ISSN 1572-8153

  19. Ponnle, A., Ogundepo, O.: Development of a computer-aided application for analyzing ECG signals and detection of cardiac arrhythmia using back propagation neural network-part I: model development. Int. J. Appl. Inf. Syst. 9 (2015). https://doi.org/10.5120/ijais15-451378

  20. van Velzen, M.H.N., Loeve, A.J., Niehof, S.P., Mik, E.G.: Increasing accuracy of pulse transit time measurements by automated elimination of distorted photoplethysmography waves. Med. Biol. Eng. Comput. 55(11), 1989–2000 (2017). https://doi.org/10.1007/s11517-017-1642-x

    Article  Google Scholar 

  21. Kortekaas, M., Niehof, S., Van Velzen, M., Galvin, E., Huygen, F., Stolker, R.: Pulse transit time as a quick predictor of a successful axillary brachial plexus block. Acta Anaesthesiologica Scandinavica 56, 1228–1233 (2012). https://doi.org/10.1111/j.1399-6576.2012.02746.x

  22. Foo, J.Y.A., et al.: Effects of poorly perfused peripheries on derived transit time parameters of the lower and upper limbs. Generic (2008). https://doi.org/10.1515/BMT.2008.023

  23. Foo, J.Y.A., Lim, C.S.: Difference in pulse transit time between populations: a comparison between Caucasian and Chinese children in Australia. J. Med. Eng. Technol. 32(2), 162–166 (2008). https://doi.org/10.1080/03091900600632694. ISSN 0309-1902

    Article  Google Scholar 

  24. Foo, J.Y.A., Lim, C.S.: Difference in pulse transit time between populations: a comparison between Caucasian and Chinese children in Australia. Generic (2007). https://doi.org/10.1515/BMT.2007.043

  25. Foo, J.Y.A.: Normality of upper and lower peripheral pulse transit time of normotensive and hypertensive children. J. Clin. Monit. Comput. 21, 243–248 (2007). https://doi.org/10.1007/s10877-007-9080-1

    Article  Google Scholar 

  26. Singha Roy, M., Gupta, R., Chandra, J.K., Das Sharma, K., Talukdar, A.: Improving photoplethysmographic measurements under motion artifacts using artificial neural network for personal healthcare. IEEE Trans. Instrum. Meas. 67(12), 2820–2829 (2018). https://doi.org/10.1109/TIM.2018.2829488. ISSN 1557-9662

    Article  Google Scholar 

  27. Mehrgardt, P., Zandavi, S.M., Poon, S.K., Kim, J., Markoulli, M., Khushi, M.: U-Net segmented adjacent angle detection (USAAD) for automatic analysis of corneal nerve structures. Data 5(2) (2020). https://doi.org/10.3390/data5020037

  28. van Gent, P., Farah, H., Nes, N., Arem, B.: Analysing noisy driver physiology real-time using off-the-shelf sensors: heart rate analysis software from the taking the fast lane project (2018). https://doi.org/10.13140/RG.2.2.24895.56485

  29. van Gent, P., Farah, H., Nes, N., Arem, B.: Heart rate analysis for human factors: development and validation of an open source toolkit for noisy naturalistic heart rate data (2018)

    Google Scholar 

  30. Kohler, B., Hennig, C., Orglmeister, R.: The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002). https://doi.org/10.1109/51.993193. ISSN 1937-4186

    Article  Google Scholar 

  31. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME–32(3), 230–236 (1985). https://doi.org/10.1109/TBME.1985.325532

    Article  Google Scholar 

  32. Gao, M., Bari Olivier, N., Mukkamala, R.: Comparison of noninvasive pulse transit time estimates as markers of blood pressure using invasive pulse transit time measurements as a reference. Physiol. Rep. 4(10), e12768 (2016). https://doi.org/10.14814/phy2.12768. ISSN 2051-817X

    Article  Google Scholar 

  33. Pimentel, M.A.F., et al.: Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Trans. Biomed. Eng. 64(8), 1914–1923 (2017). https://doi.org/10.1109/TBME.2016.2613124. ISSN 1558-2531

    Article  Google Scholar 

  34. Rajala, S., Ahmaniemi, T., Lindholm, H., Taipalus, T.: Pulse arrival time (PAT) measurement based on arm ECG and finger PPG signals - comparison of PPG feature detection methods for PAT calculation, vol. 2017 (2017). https://doi.org/10.1109/EMBC.2017.8036809

  35. Becker, D.: Fundamentals of electrocardiography interpretation. Anesth. Prog. 53, 53–63 (2006). https://doi.org/10.2344/0003-3006(2006)53[53:FOEI]2.0.CO;2. quiz 64

    Article  Google Scholar 

  36. Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., Tarassenko, L.: Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J. Biomed. Health Inform. 19(3), 832–838 (2015). https://doi.org/10.1109/JBHI.2014.2338351. ISSN 2168-2208

    Article  Google Scholar 

  37. Lin, Y.-T., Lo, Y.-L., Lin, C.-Y., Frasch, M.G., Wu, H.-T.: Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data. PLOS ONE 14(9), e0221319 (2019). https://doi.org/10.1371/journal.pone.0221319

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the University of Sydney Cardiovascular Initiative funding. Dr Withana is the recipient of an Australian Research Council Discovery Early Career Award (DE200100479) funded by the Australian Government.

Author information

Authors and Affiliations

  1. School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia

    Philip Mehrgardt, Matloob Khushi, Anusha Withana & Simon Poon

  2. Cardiovascular Initiative, The University of Sydney, Sydney, NSW, 2008, Australia

    Anusha Withana

Authors
  1. Philip Mehrgardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matloob Khushi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anusha Withana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simon Poon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philip Mehrgardt .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mehrgardt, P., Khushi, M., Withana, A., Poon, S. (2020). Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63836-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63835-1

  • Online ISBN: 978-3-030-63836-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation