Advertisement

An ECG Morphological Analysis Algorithm for Hybrid Patient Monitoring

  • A. Raza
  • Paul FaragoEmail author
  • M. Cirlugea
  • S. Hintea
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 71)

Abstract

Screening and diagnosis in ubiquitous healthcare environments is performed by the medical staff in the presence of a variety of medical data originating from continuous in vivo patient monitoring. This assumes the correlation of data, such as vital signs, biopotentials, activity tracking, bio-markers, etc., in the context of hybrid clinical monitoring. The hybrid patient monitoring setup envisioned in this work targets the simultaneous assessment of body fluids via electro-optical means and of electrophysiological data, i.e. electrocardiogram, via biopotential monitoring. Accordingly, the aim of this paper is to propose an electrocardiogram morphological analysis algorithm applicable in the framework of the envisioned hybrid monitoring setup. Electrocardiogram morphological analysis is performed in time domain in two stages. The R, S and Q waves are first identified via comparison to a threshold value. Then, the P and T waves are identified via cross-correlation to a reference signal. The novelty of this work is that cross-correlation is performed after the QRS complex was eliminated from the electrocardiogram. Expectedly, this leads to improved results in wave identification and to a reduction of false detections. Matlab simulation results validate the proposed procedure.

Keywords

Biomedical equipment Biomedical monitoring Biomedical signal processing Signal processing algorithms 

Notes

Acknowledgements

This study was supported by the COFUND-ERA-HDHL ERANET Project, European and International Cooperation—Subprogram 3.2—Horizon 2020, PNCDI III Program—Biomarkers for Nutrition and Health—“Innovative technological approaches for validation of salivary AGEs as novel biomarkers in evaluation of risk factors in diet-related diseases”, no 25/1.09.2017.

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Imani, S., Bandodkar, A.J., et al.: A wearable chemical–electrophysiological hybrid biosensing system for real-time health and fitness monitoring. Nature Commun. vol. 7 (2016)Google Scholar
  2. 2.
    Floriano, P.N., Christodoulides, N., et al.: Use of saliva-based nano-biochip tests for acute myocardial infarction at the point of care: a feasibility study. Clin. Chem. 55(8), 1530–1538 (2009)CrossRefGoogle Scholar
  3. 3.
    Sarpeshkar, R.: Ultra low power bioelectronics fundamentals, biomedical applications, and bio-inspired systems. Cambridge University Press (2010)Google Scholar
  4. 4.
    Odame, K., Du, D.: Towards a smart sensor interface for wearable cough monitoring, 2013. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2013)Google Scholar
  5. 5.
    Djurić, B., Suzić, S., et al.: An improved design of optical sensor for long-term measurement of arterial blood flow waveform. Biomed. Microdevices 19(3), 48 (2017).  https://doi.org/10.1007/s10544-017-0196-xCrossRefGoogle Scholar
  6. 6.
    Kwon, K., Park, S.: An optical sensor for the non-invasive measurement of the blood oxygen saturation of an artificial heart according to the variation of hematocrit. Sens. Actuators A 43(1–3), 49–54 (1994)CrossRefGoogle Scholar
  7. 7.
    Tan, W., Sabet, L., et al.: Optical protein sensor for detecting cancer markers in saliva. Biosens. Bioelectron. 24(2), 266–271 (2009)CrossRefGoogle Scholar
  8. 8.
    Alomari, M., Liu, G., et al.: A portable optical human sweat sensor. J Appl Phys 116, 18.  https://doi.org/10.1063/1.4901332 (2014)
  9. 9.
    Thakor, N.V.: Biopotentials and electrophysiology measurement. CRC Press LLC (2000)Google Scholar
  10. 10.
    Ramli, A.B., Ahmad, P.A.: Correlation analysis for abnormal ECG signal features extraction, NCTT 2003. In: Proceedings of 4th National Conference of Telecommunication Technology.  https://doi.org/10.1109/nctt.2003.1188342 (2003)
  11. 11.
    Last, T., Nugent, C.D., et al.: Multi-component based cross correlation beat detection in electrocardiogram analysis. Biomed. Eng. Online.  https://doi.org/10.1186/1475-925x-3-26 (2004)
  12. 12.
    Joshi, S.L., Vatti, R.A., et al.: A survey on ECG signal denoising techniques, 2013. In: International Conference on Communication Systems and Network Technologies (CSNT), pp. 60–64 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Bases of Electronics Department, Faculty of ElectronicsTelecommunications and Information Technology, Technical University of Cluj-NapocaCluj-NapocaRomania

Personalised recommendations