Signal, Image and Video Processing

, Volume 13, Issue 3, pp 475–482 | Cite as

Nonintrusive heart rate measurement using ballistocardiogram signals: a comparative study

  • Ibrahim SadekEmail author
  • Jit Biswas
Original Paper


Nonintrusive monitoring and long-term monitoring of vital signs are essential requirements for early diagnosis and prevention due to many reasons, one of the most important being improving the quality of life. In this paper, we present a comparative study using various algorithms, i.e., wavelet analysis, cepstrum, fast Fourier transform, and autocorrelation function for heart rate measurement. The heart rate was measured from noisy ballistocardiogram signals acquired from 50 subjects in a sitting position using a massage chair. The signals were unobtrusively collected from a microbend fiber-optic sensor embedded within the headrest of the chair and then transmitted to a computer through a Bluetooth connection. The multiresolution analysis of the maximal overlap discrete wavelet transform was implemented for heart rate measurement. The error between the proposed method and the reference electrocardiogram is estimated in beats per minute using the mean absolute error in which the system achieved relatively good results (\(10.12\pm 4.69\)) despite the remarkable amount of motion artifact produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. In contrast, the error between the proposed method and the reference signal was very large when other algorithms, i.e., cepstrum, fast Fourier transform, and autocorrelation function, were implemented for heart rate measurement.


Ballistocardiography Vital signs Microbend fiber optic E-health Wavelet analysis 


  1. 1.
    Pinheiro, E., Postolache, O., Girão, P.: Theory and developments in an unobtrusive cardiovascular system representation: ballistocardiography. Open Biomed. Eng. J. 4, 201 (2010)CrossRefGoogle Scholar
  2. 2.
    Vogt, E., MacQuarrie, D., Neary, J.P.: Using ballistocardiography to measure cardiac performance: a brief review of its history and future significance. Clin. Physiol. Funct. Imaging 32(6), 415–420 (2012)CrossRefGoogle Scholar
  3. 3.
    Giovangrandi, L., Inan, O.T., Wiard, R.M., Etemadi, M., Kovacs, G.T.A.: Ballistocardiography—a method worth revisiting. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 4279–4282 Aug 2011Google Scholar
  4. 4.
    Inan, O.T., Baran Pouyan, M., Javaid, A.Q., Dowling, S., Etemadi, M., Dorier, A., Heller, J.A., Bicen, A.O., Roy, S., De Marco, T., Klein, L.: Novel wearable seismocardiography and machine learning algorithms can assess clinical status of heart failure patients. Circ. Heart Fail. 11(1), e004313 (2018)CrossRefGoogle Scholar
  5. 5.
    Krej, M., Dziuda, Ł., Skibniewski, F.W.: A method of detecting heartbeat locations in the ballistocardiographic signal from the fiber-optic vital signs sensor. IEEE J. Biomed. Health Inform. 19(4), 1443–1450 (2015)CrossRefGoogle Scholar
  6. 6.
    Nedoma, J., Fajkus, M., Martinek, R., Kepak, S., Cubik, J., Zabka, S., Vasinek, V.: Comparison of BCG, PCG and ECG signals in application of heart rate monitoring of the human body. In: 2017 40th International Conference on Telecommunications and Signal Processing (TSP), IEEE, pp. 420–424 (2017)Google Scholar
  7. 7.
    Zink, M.D., Brüser, C., Stüben, B.O., Napp, A., Stöhr, R., Leonhardt, S., Marx, N., Mischke, K., Schulz, J.B., Schiefer, J.: Unobtrusive nocturnal heartbeat monitoring by a ballistocardiographic sensor in patients with sleep disordered breathing. Sci. Rep. 7(1), 13175 (2017)CrossRefGoogle Scholar
  8. 8.
    Alvarado-Serrano, C., Luna-Lozano, P.S., Pallàs-Areny, R.: An algorithm for beat-to-beat heart rate detection from the bcg based on the continuous spline wavelet transform. Biomed. Signal Process. Control 27(Supplement C), 96–102 (2016)CrossRefGoogle Scholar
  9. 9.
    Tantawi, M.M., Revett, K., Salem, A.B., Tolba, M.F.: A wavelet feature extraction method for electrocardiogram (ecg)-based biometric recognition. Signal Image Video Process. 9(6), 1271–1280 (2015)CrossRefGoogle Scholar
  10. 10.
    Fathi, A., Faraji-kheirabadi, F.: Ecg compression method based on adaptive quantization of main wavelet packet subbands. Signal Image Video Process. 10(8), 1433–1440 (2016)CrossRefGoogle Scholar
  11. 11.
    Sharma, L.D., Sunkaria, R.K.: Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process. 12(2), 199–206 (2018)CrossRefGoogle Scholar
  12. 12.
    Katz, Y., Karasik, R., Shinar, Z.: Contact-free piezo electric sensor used for real-time analysis of inter beat interval series. In: 2016 Computing in Cardiology Conference (CinC), 769–772 Sept 2016Google Scholar
  13. 13.
    Brüser, C., Kortelainen, J.M., Winter, S., Tenhunen, M., Pärkkä, J., Leonhardt, S.: Improvement of force-sensor-based heart rate estimation using multichannel data fusion. IEEE J. Biomed. Health Inform. 19(1), 227–235 (2015)CrossRefGoogle Scholar
  14. 14.
    Sadek, I., Seet, E., Biswas, J., Abdulrazak, B., Mokhtari, M.: Nonintrusive vital signs monitoring for sleep apnea patients: a preliminary study. IEEE Access 6, 2506–2514 (2018)CrossRefGoogle Scholar
  15. 15.
    Sadek, I., Mohktari, M.: Nonintrusive remote monitoring of sleep in home-based situation. J. Med. Syst. 42(4), 64 (2018)CrossRefGoogle Scholar
  16. 16.
    Sadek, I., Biswas, J., Yongwei, Z., Haihong, Z., Maniyeri, J., Zhihao, C., Teng, T.J., Huat, N.S., Mokhtari, M.: Sensor data quality processing for vital signs with opportunistic ambient sensing. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2484–2487 Aug 2016Google Scholar
  17. 17.
    Sadek, I., Biswas, J., Abdulrazak, B., Haihong, Z., Mokhtari, M.: Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position. In: 2017 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), 289–292 Feb 2017Google Scholar
  18. 18.
    Percival, D.B., Walden, A.T.: Wavelet Methods for Time Series Analysis, vol. 4. Cambridge University Press, Cambridge (2006)Google Scholar
  19. 19.
    Oppenheim, A.V., Schafer, R.W.: From frequency to quefrency: a history of the cepstrum. IEEE Signal Process. Mag. 21(5), 95–106 (2004)Google Scholar
  20. 20.
    Kortelainen, J.M., Virkkala, J.: Fft averaging of multichannel bcg signals from bed mattress sensor to improve estimation of heart beat interval. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6685–6688 Aug 2007Google Scholar
  21. 21.
    Kortelainen, J.M., van Gils, M., Pärkkä, J.: Multichannel bed pressure sensor for sleep monitoring. In: 2012 Computing in Cardiology, pp. 313–316 (2012)Google Scholar
  22. 22.
    Zhu, Y., Fook, V.F.S., Jianzhong, E.H., Maniyeri, J., Guan, C., Zhang, H., Jiliang, E.P., Biswas, J.: Heart rate estimation from fbg sensors using cepstrum analysis and sensor fusion. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5365–5368 Aug 2014Google Scholar
  23. 23.
    Sadek, I., Biswas, J., Fook, V.F.S., Mokhtari, M.: Automatic heart rate detection from fbg sensors using sensor fusion and enhanced empirical mode decomposition. In: 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 349–353 (2015)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Image and Pervasive Access LaboratoryCNRS UMI 2955SingaporeSingapore
  2. 2.ST Electronics-SUTD Cyber Security LaboratorySingapore University of Technology and DesignSingaporeSingapore
  3. 3.Information Systems Technology and Design, Science and MathSingapore University of Technology and Design (SUTD)SingaporeSingapore

Personalised recommendations