Skip to main content
SpringerLink
Log in

Comparison of Indices Derived from Poincaré Maps on Electrocardiograms and Seismocardiograms

  • Conference paper
  • First Online:
Current Trends in Biomedical Engineering and Bioimages Analysis (PCBEE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1033))

Included in the following conference series:

Abstract

Heart rate variability (HRV) is the physiological variation of time between heart beats caused by the activity of autonomic nervous system. Heart rate variability analysis has found various applications in the diagnosis and treatment of different clinical and functional conditions. One of the prominent approaches in HRV analysis are Poincaré map. HRV analysis is traditionally performed on electrocardiograms (ECG) although seismocardiograms can also be used. In this study we compare indices derived from Poincaré maps on electrocardiograms and seismocardiograms found in CEBS database available on PhysioNet.org. Poincaré map is a non-linear method of HRV analysis which uses diagrams in which inter-beat intervals are plotted as a function of previous inter-beat intervals. We found that there are no significant differences of indices of Poincaré maps calculated on electrocardiograms and seismocardiograms, which indicates the reliability of seismocardiogram as a source signal in non-linear HRV analysis using Poincaré maps.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • 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

References

  1. Barauskiene, V., Rumbinaite, E., Karuzas, A., Martinkute, E., Puodziukynas, A.: Importance of heart rate variability in patients with atrial fibrillation. J. Cardiol. Clin. Res. 4(6), 1080 (2016)

    Google Scholar 

  2. Biala, T., Dodge, M., Schlindwein, F.S., Wailoo, M.: Heart rate variability using poincaré plots in 10 year old healthy and intrauterine growth restricted children with reference to maternal smoking habits during pregnancy. In: 2010 Computing in Cardiology, pp. 971–974 (2010)

    Google Scholar 

  3. Brennan, M., Palaniswami, M., Kamen, P.: Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48(11), 1342–1347 (2001). https://doi.org/10.1109/10.959330

    Article  Google Scholar 

  4. Budzianowski, Z., Tkacz, E., Oleksy, W., Garbacik, M.: The higher-order spectra as a tool for the identification of patients diagnosed with various cardiac diseases. In: Gzik, M., Tkacz, E., Paszenda, Z., Piętka, E. (eds.) Innovations in Biomedical Engineering, pp. 193–203. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70063-2_21

    Chapter  Google Scholar 

  5. Castiglioni, P., Faini, A., Parati, G., Rienzo, M.D.: Wearable seismocardiography. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3954–3957 (2007). https://doi.org/10.1109/IEMBS.2007.4353199

  6. Castiglioni, P., Meriggi, P., Rizzo, F., Vaini, E., Faini, A., Parati, G., Merati, G., Rienzo, M.D.: Cardiac sounds from a wearable device for sternal seismocardiography. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4283–4286 (2011). https://doi.org/10.1109/IEMBS.2011.6091063

  7. García-González, M.A., Argelagós, A., Fernández-Chimeno, M., Ramos-Castro, J.: Differences in QRS locations due to ECG lead: relationship with breathing. In: Roa Romero, L.M. (ed.) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, pp. 962–964. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00846-2_238

    Chapter  Google Scholar 

  8. García-González, M.A., Argelagós-Palau, A., Fernández-Chimeno, M., Ramos-Castro, J.: A comparison of heartbeat detectors for the seismocardiogram. In: Computing in Cardiology 2013, pp. 461–464 (2013)

    Google Scholar 

  9. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). https://doi.org/10.1161/01.CIR.101.23.e215. Circulation Electronic Pages: http://circ.ahajournals.org/content/101/23/e215.fullPMID:1085218

  10. Hamilton, P.S., Tompkins, W.J.: Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. BME–33(12), 1157–1165 (1986). https://doi.org/10.1109/TBME.1986.325695

    Article  Google Scholar 

  11. Karmakar, C.K., Gubbi, J., Khandoker, A.H., Palaniswami, M.: Analyzing temporal variability of standard descriptors of poincaré plots. J. Electrocardiol. 43(6), 719–724 (2010). https://doi.org/10.1016/j.jelectrocard.2010.09.001. http://www.sciencedirect.com/science/article/pii/S0022073610003821

    Article  Google Scholar 

  12. Kitlas Golińska, A.: Poincaré plots in analysis of selected biomedical signals. Stud. Logic Grammar Rhetoric 35(1), 117–127 (2013). https://doi.org/10.2478/slgr-2013-0031

    Article  Google Scholar 

  13. Kostka, P.S., Tkacz, E.J.: Multi-sources data analysis with sympatho-vagal balance estimation toward early bruxism episodes detection. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6010–6013 (2015). https://doi.org/10.1109/EMBC.2015.7319761

  14. Landreani, F., Morri, M., Martin-Yebra, A., Casellato, C., Pavan, E., Frigo, C., Caiani, E.G.: Ultra-short-term heart rate variability analysis on accelerometric signals from mobile phone. In: 2017 E-Health and Bioengineering Conference (EHB), pp. 241–244 (2017). https://doi.org/10.1109/EHB.2017.7995406

  15. Laurin, A., Blaber, A., Tavakolian, K.: Seismocardiograms return valid heart rate variability indices. Comput. Cardiol. 2013, 413–416 (2013)

    Google Scholar 

  16. Li, Y., Tang, X., Xu, Z.: An approach of heartbeat segmentation in seismocardiogram by matched-filtering. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, pp. 47–51 (2015). https://doi.org/10.1109/IHMSC.2015.157

  17. Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K.R., Iellamo, F.: Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior. Neurosci. Biobehav. Rev. 33(2), 71–80 (2009). The Inevitable Link between Heart and Behavior: New Insights from Biomedical Research and Implications for Clinical Practice. https://doi.org/10.1016/j.neubiorev.2008.07.006, http://www.sciencedirect.com/science/article/pii/S0149763408001176

    Article  Google Scholar 

  18. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME–32(3), 230–236 (1985)

    Article  Google Scholar 

  19. Pandia, K., Inan, O.T., Kovacs, G.T.A., Giovangrandi, L.: Extracting respiratory information from seismocardiogram signals acquired on the chest using a miniature accelerometer. Physiol. Meas. 33(10), 1643–1660 (2012). https://doi.org/10.1088/0967-3334/33/10/1643

    Article  Google Scholar 

  20. Pumprla, J., Howorka, K., Groves, D., Chester, M., Nolan, J.: Functional assessment of heart rate variability: physiological basis and practical applications. Int. J. Cardiol. 84(1), 1–14 (2002). https://doi.org/10.1016/S0167-5273(02)00057-8. http://www.sciencedirect.com/science/article/pii/S0167527302000578

    Article  Google Scholar 

  21. Ramos-Castro, J., Moreno, J., Miranda-Vidal, H., García-González, M.A., Fernández-Chimeno, M., Rodas, G., Capdevila, L.: Heart rate variability analysis using a seismocardiogram signal. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5642–5645 (2012). https://doi.org/10.1109/EMBC.2012.6347274

  22. Ruan, X., Liu, C., Liu, C., Wang, X., Li, P.: Automatic detection of atrial fibrillation using r-r interval signal. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), vol. 2, pp. 644–647 (2011). https://doi.org/10.1109/BMEI.2011.6098492

  23. Saykrs, B.: Analysis of heart rate variability. Ergonomics 16(1), 17–32 (1973). https://doi.org/10.1080/00140137308924479. PMID: 4702060

    Article  Google Scholar 

  24. Siecinski, S., Kostka, P.S., Tkacz, E.J.: Heart rate variability analysis on CEBS database signals. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5697–5700 (2018). https://doi.org/10.1109/EMBC.2018.8513551

  25. Tadi, M.J., Koivisto, T., Pänkäälä, M., Paasio, A.: Accelerometer-based method for extracting respiratory and cardiac gating information for dual gating during nuclear medicine imaging. Int. J. Biomed. Imaging 2014(690124), 1–11 (2014). https://doi.org/10.1155/2014/690124

    Article  Google Scholar 

  26. Tadi, M.J., Lehtonen, E., Koivisto, T., Pänkäälä, M., Paasio, A., Teräs, M.: Seismocardiography: toward heart rate variability (HRV) estimation. In: 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, pp. 261–266 (2015). https://doi.org/10.1109/MeMeA.2015.7145210

  27. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology: Heart rate variability. standards of measurement, physiological interpretation, and clinical use. Circulation 93, 1043–1065 (1996). https://doi.org/10.1161/01.CIR.93.5.1043

    Article  Google Scholar 

  28. Tkacz, E., Budzianowski, Z., Oleksy, W.: The higher-order spectra as a tool for assessing the progress in rehabilitation of patients after ischemic brain stroke. In: Rocha, Á., Guarda, T. (eds.) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018), pp. 874–882. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_83

    Chapter  Google Scholar 

  29. Wedekind, D.: qrsdetector (2014). https://github.com/danielwedekind/qrsdetector

  30. Zanetti, J.M., Poliac, M.O., Crow, R.S.: Seismocardiography: waveform identification and noise analysis. In: [1991] Proceedings Computers in Cardiology, pp. 49–52 (1991). https://doi.org/10.1109/CIC.1991.169042

  31. Zanetti, J.M., Salerno, D.M.: Seismocardiography: a technique for recording precordial acceleration. In: [1991] Computer-Based Medical Systems, Proceedings of the Fourth Annual IEEE Symposium, pp. 4–9 (1991). https://doi.org/10.1109/CBMS.1991.128936

  32. Zanetti, J.M., Tavakolian, K.: Seismocardiography: past, present and future. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7004–7007 (2013). https://doi.org/10.1109/EMBC.2013.6611170

  33. İşler, Y., Kuntalp, M.: Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Comput. Biol. Med. 37(10), 1502–1510 (2007). QT Variability & Heart Rate Variability. https://doi.org/10.1016/j.compbiomed.2007.01.012. http://www.sciencedirect.com/science/article/pii/S0010482507000285

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Biomedical Engineering, Department of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland

    Szymon Sieciński, Paweł Kostka, Natalia Piaseczna & Marta Wadas

Authors
  1. Szymon Sieciński
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paweł Kostka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natalia Piaseczna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marta Wadas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Szymon Sieciński .

Editor information

Editors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Gora, Zielona Góra, Poland

    Józef Korbicz

  2. Committee of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

    Roman Maniewski

  3. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

  4. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Marek Kowal

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

Sieciński, S., Kostka, P., Piaseczna, N., Wadas, M. (2020). Comparison of Indices Derived from Poincaré Maps on Electrocardiograms and Seismocardiograms. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29885-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29884-5

  • Online ISBN: 978-3-030-29885-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation