Skip to main content

Advertisement

SpringerLink
Log in

Improving the Accuracy of Biosignal Analysis Using BCG by Applying a Signal-to-Noise Ratio and Similarity-Based Channel Selection Algorithm

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

As the socio-economic environment has changed, the number of single and elderly households has increased, and the demand for maintaining a long and healthy life has been increased by continuously monitoring and managing physical conditions against unexpected accidents that may occur when living alone at home. In response to these demands, it is necessary to develop services that provide personalized healthcare services by recording and analyzing living patterns and biometric information in an unconscious way. In this paper, we propose an accuracy improvement method using similarity and Signal-to-noise ratio analysis for BCG measurement method using piezo sensors and a system for continuous personal health monitoring in home without user awareness. Heart rate and respiration rate were derived using the acquired BCG data, and the biometric information is stored symmetrically for personal information security.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Lim CY (2018) Realization of gait pattern detection and bio-signal monitoring system using smart pad of pressure sensor type. J Inst Electron Eng Korea 1:85–86

    Google Scholar 

  2. Gubner RS, Rodstein M, Ungerleider HE (1953) Ballistocardiography: An appraisal of technic, physiologic principles, and clinical value. Circulation 7(2):268–286

    Article  Google Scholar 

  3. Pinheiro E, Postolache O, Girão P (2010) “Theory and developments in an unobtrusive cardiovascular system representation: ballistocardiography. The open Biomed Eng J 4:201

    Article  Google Scholar 

  4. Inan OT, et al (2015) Systems and methods for monitoring heart function. U.S. Patent 9215991, Dec 22, 2015

  5. Da He D, Winokur ES, Sodini CG (2011) A continuous, wearable, and wireless heart monitor using head ballistocardiogram (BCG) and head electrocardiogram (ECG). In: 2011 Annual International Conference of the IEEE engineering in medicine and biology society, 2011

  6. Inan OT et al (2009) Non-invasive cardiac output trending during exercise recovery on a bathroom-scale-based ballistocardiograph. Physiol Meas 30(3):261

    Article  Google Scholar 

  7. Etemadi M et al (2011) Rapid assessment of cardiac contractility on a home bathroom scale. IEEE Trans Inf Technol Biomed 15(6):864–869

    Article  MathSciNet  Google Scholar 

  8. Javaid AQ, Ashouri H, Inan OT (2016) Estimating systolic time intervals during walking using wearable ballistocardiography. In: 2016 IEEE-EMBS International Conference on biomedical and health informatics (BHI), 2016

  9. Starr I et al (1939) Studies on the estimation of cardiac ouptut in man, and of abnormalities in cardiac function, from the heart’s recoil and the blood’s impacts; the ballistocardiogram. Am J Physiol-Legacy Content 127(1):1–28

    Article  Google Scholar 

  10. Starr I (1965) Progress towards a physiological cardiology: a second essay on the ballistocardiogram. Ann Intern Med 63(6):1079–1105

    Article  Google Scholar 

  11. Tadi MJ, et al (2016) Gyrocardiography: A new non-invasive approach in the study of mechanical motions of the heart. Concept, method and initial observations. In: 2016 38th Annual International Conference of the IEEE engineering in medicine and biology society (EMBC), 2016.

  12. Lee J et al (2019) Design of a symmetry protocol for the efficient operation of IP cameras in the IoT environment. Symmetry 11(3):361

    Article  Google Scholar 

  13. Alametsä J, et al (2004) The potential of EMFi sensors in heart activity monitoring. In: Proc. 2nd OpenECG Workshop: integration ECG into the EHR interoperability of ECG Device Syst. Berlin, Germany, 2004

  14. Redmond SJ, Heneghan C (2006) Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea. IEEE Trans Biomed Eng 53(3):485–496

    Article  Google Scholar 

  15. Alihanka J, Vaahtoranta K, Saarikivi I (1981) A new method for long-term monitoring of the ballistocardiogram, heart rate, and respiration. Am J Physiol-Regul Integr Comp Physiol 240(5):R384–R392

    Article  Google Scholar 

  16. Paalasmaa J, Waris M, Toivonen H, Leppäkorpi L, Partinen M (2012) Unobtrusive online monitoring of sleep at home. In: 34th IEEE EMBC, August 28–September 1, San Diego, USA, 2012

  17. Lekkala J, Tuppurainen J, Paajanen M (2003) Material and operational properties of large area membrane type sensors for smart environments. In: XVII IMEKO World Congress, June 22–27, Dubrovnik, Croatia, 2003

  18. Vehkaoja A, Kontunen A, Lekkala J (2015) Effects of sensor type and sensor location on signal quality in bed mounted ballistocardiographic heart rate and respiration monitoring. In: Proc. Of 37th Annu. Int. Conf. of IEEE engineering in medicine and biological society, Milano, Italy, 2015

  19. Przystup P, Bujnowski A, Ruminski J, Wtorek J (2013) A multisensor detector of a sleep apnea for using at home. In: 2013 6th International Conference on Human System Interactions, HSI 2013.

  20. Brink M, Müller CH, Schierz C (2006) Contact-free measurement of heart rate, respiration rate, and body movements during sleep. Behav Res Methods 38(3):511–521

    Article  Google Scholar 

  21. Juha MK, Mark G, Juha P (2012) Multichannel bed pressure sensor for sleep monitoring. In: IEEE 2012 Computing in Cardiology, 9–12 Sept., Krakow, Poland, 2012

  22. Kortelainen J, Virkkala J (2007) FFT averaging of multichannel BCG signals from bed mattress sensor to improve estimation of heart beat interval. In: 29th IEEE EMBC, August 23–26, Lyon, France, 2007

  23. Brüser C, Stadlthanner K, de Waele S, Leonhardt S (2011) Adaptive beat-to-beat heart rate estimation in ballistocardiograms. IEEE Trans Inf Technol Biomed 15(5):778–786

    Article  Google Scholar 

  24. Noh SW (2014) Applications of new ballistocardiograph systems. Ph.D. Dissertation. University of Seoul National University, KR., 2014

  25. Sörnmo L, Laguna P (2005) ECG signal processing. Bioelectr Signal Process Cardiac Neurol Appl 1(1):453–566

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Community business revitalization business Program (P0002366, "Development of Modular ICT Healthcare Open Platform for Social and Economic Enterprise Scale-up") funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea)

Author information

Authors and Affiliations

  1. Korea Electronics Technology Institute, 13509, Seongnam, South Korea

    Chulseung Yang, Gi Won Ku & Jeong-Gi Lee

  2. Department of Electronics and Electrical Engineering, Dankook University, Yongin, 16890, South Korea

    Chulseung Yang & Kyungho Kim

Authors
  1. Chulseung Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gi Won Ku
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeong-Gi Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyungho Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: CY and GK; Methodology, CY and GK; Software: JL; Validation: CY and GK; Formal analysis: JL; Investigation: CY and GK; Resources: JL; Data curation: JL and Gk; Writing-original draft preparation: CY and GK; Writing-review and editing: KK; Visualization: GK and JL; Supervision: KK; Project administration: CY and KK.

Corresponding author

Correspondence to Kyungho Kim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, C., Ku, G.W., Lee, JG. et al. Improving the Accuracy of Biosignal Analysis Using BCG by Applying a Signal-to-Noise Ratio and Similarity-Based Channel Selection Algorithm. J. Electr. Eng. Technol. 16, 1043–1050 (2021). https://doi.org/10.1007/s42835-020-00601-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-020-00601-8

Keywords

Search

Navigation