Heart Beat Detection from Smartphone SCG Signals: Comparison with Previous Study on HR Estimation

  • Szymon SiecińskiEmail author
  • Paweł Kostka
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 925)


Seismocardiography (SCG) is a non-invasive method of analyzing and recording cardiovascular activity as vibrations transmitted to the chest wall. Mobile devices offer the possibility to monitor health parameters thanks to embedded sensors. Various applications have been proposed for SCG, including heart rate calculation. Our aim is to detect heart beat on seismocardiograms using improved algorithm and compare its performance with results obtained in previous study.

Algorithm proposed in this study consists of signal preprocessing, RMS envelope calculation and peak finding. Algorithm performance was measured as sensitivity (Se) and positive predictive value (PPV) of beat detection on 4 signals acquired from 4 subjects.

We achieved average \(Se = 0.994\) and \(PPV = 0.966\) and in the best case \(Se = 0.995\) and \(PPV = 0.970\). Results prove major improvement of beat detection from smartphone seismocardiograms since the previous study.


Seismocardiography AO detection Heart beat detection Smartphone 


  1. 1.
    Bosch Sensortec: BMA255, Digital, Triaxial Accelerometer. BMA255 Datasheet, 1 August 2014Google Scholar
  2. 2.
    Bosch Sensortec: BMA255 (n.d.). Accessed 24 Jan 2018
  3. 3.
    Bruining, N., et al.: Acquisition and analysis of cardiovascular signals on smartphones: potential, pitfalls and perspectives. Eur. J. Prev. Cardiol. 21(2 Suppl.), 4–13 (2014). By the Task Force of the e-Cardiology Working Group of the European Society of CardiologyCrossRefGoogle Scholar
  4. 4.
    Caetano, M.F., Rodet, X.: Improved estimation of the amplitude envelope of time domain signals using true envelope cepstral smoothing. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Czech Republic, May 2011, pp. 11–21 (2011)Google Scholar
  5. 5.
    De Luca, C.J.: Electromyography. In: Webster, J.G. (ed.) Encyclopedia of Medical Devices and Instrumentation, pp. 98–109. Wiley (2006).
  6. 6.
    Di Rienzo, M., Vaini, E., Castiglioni, P., Meriggi, P., Rizzo, F.: Beat-to-beat estimation of LVET and QS2 indices of cardiac mechanics from wearable seismocardiography in ambulant subjects. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, Osaka, pp. 7017–7020 (2013).
  7. 7.
    Inan, O.T., Migeotte, P.F., Park, K.S., Etemadi, M., Tavakolian, K., Casanella, R., Zanetti, J., Tank, J., Funtova, I., Prisk, G.K., Di Rienzo, M.: Ballistocardiography and seismocardiography: a review of recent advances. IEEE J. Biomed. Health Inform. 19(4), 1414–27 (2015)CrossRefGoogle Scholar
  8. 8.
    Komorowski, D., Pietraszek, S., Darlak, M.: Pressure and output flow estimation of pneumatically controlled ventricular assist device (VAD) with the help of both acceleration and gyro sensors. In: World Congress on Medical Physics and Biomedical Engineering 2006, IFMBE Proceedings, vol. 14, no. Part 7, pp. 719–722 (2007)Google Scholar
  9. 9.
    Korzeniowska-Kubacka, I.: Sejsmokardiografia—nowa nieinwazyjna metoda oceny czynności lewej komory w chorobie niedokrwiennej serca. Folia Cardiol. 10(3), 265–268 (2003)Google Scholar
  10. 10.
    Kostka, P., Tkacz, E.: Modern MEMS acceleration sensors in tele-monitoring systems for movement parameters and human fall remote detection. In: Kapczyński, A., Tkacz, E., Rostanski, M. (eds.) Internet - Technical Developments and Applications 2. Advances in Intelligent and Soft Computing, vol. 118, pp. 271–277. Springer, Heidelberg (2012). Scholar
  11. 11.
    Landreani, F., Martin-Yebra, A., Casellato, C., Frigo, C., Pavan, E., Migeotte, P.F., Caiani, E.G.: Beat-to-beat heart rate detection by smartphone’s accelerometers: validation with ECG. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, FL, Orlando, pp. 525–528 (2016)Google Scholar
  12. 12.
    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, Sinaia 2017, pp. 241–244 (2017).
  13. 13.
    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, August 2015, vol. 2, pp. 47–51.
  14. 14.
    Ramos-Castro, J., Moreno, J., Miranda-Vidal, H., García-González, M., 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, EMBC, August 2012, pp. 5642–5645 (2012)Google Scholar
  15. 15.
    Pouymiro, I.R., Cordova, E.V., Perez, F.E.V.: Robust detection of AO and IM points in the seismocardiogram using CWT. IEEE Lat. Am. Trans. 14(11), 4468–4473 (2016). Scholar
  16. 16.
    Wered Software: Sensor Multitool (Version 1.3.0) (2017). Accessed 23 Jan 2018
  17. 17.
    Siecinski, S., Kostka, P.: Determining heart rate beat-to-beat from smartphone seismocardiograms: preliminary studies. In: Innovations in Biomedical Engineering. Advances in Intelligent Systems and Computing, vol. 623, pp. 133–140. Springer, Cham (2018). Scholar
  18. 18.
    Siecinski, S., Kostka, P.: Influence of gravitational offset removal on heart beat detection performance from Android smartphone seismocardiograms. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technology in Biomedicine, ITIB 2018. Advances in Intelligent Systems and Computing, vol. 762, pp. 337–344. Springer, Cham (2019). Scholar
  19. 19.
    Siecinski, S., Kostka, P.S., Tkacz, E.J.: Heart rate variability analysis on CEBS database signals. In: 40th Annual International Conference of IEEE EMBS, Honolulu, HI, USA, 17-21 July 2018, pp. 5697–5700.
  20. 20.
    Shafiq, G., et al.: Automatic identification of systolic time intervals in seismocardiogram. Sci. Rep. 6, 37524 (2016). Scholar
  21. 21.
    Tadi, M.J., et al.: A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms. Physiol. Meas. 37, 1885 (2016)CrossRefGoogle Scholar
  22. 22.
    Zanetti, J.M., Salerno, D.M.: Seismocardiography: a technique for recording precordial acceleration. In: Proceedings of Fourth Annual IEEE Symposium of Computer-Based Medical Systems, Baltimore, MD, USA, pp. 4–9 (1991)Google Scholar
  23. 23.
    Zanetti, J.M., Salerno, D.M.: Seismocardiography: waveform identification and noise analysis. In: Computers in Cardiology, Venice, Italy, pp. 49–52 (1991)Google Scholar
  24. 24.
    Zanetti, J.M., Tavakolian, K.: Seismocardiography: past, present and future. In: Proceedings of 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, 3–7 July (2013)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biosensors and Biomedical Signals ProcessingSilesian University of TechnologyZabrzePoland

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