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A Combination Solution for Sleep Apnea and Heart Rate Detection Based on Accelerometer Tracking

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Future Data and Security Engineering (FDSE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

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Abstract

In this paper, we propose a combined solution to collect and detect both the heart rate and the obstruction sleep apnea using only vibration signals measured at the patient’s neck. Our proposed wearable device can capture vibration signals caused by the respiratory activities and the blood flows in the common carotid artery (CCA) and the internal jugular vein (IJV) on the patient’s neck area during sleeping. The data are sent to a server via WIFI connection and stored in a database for further analysis. Our system is accurate and low-cost for capturing the signals and monitoring many patients simultaneously. Moreover, the paper approach also goes deeper into signal processing by using a combination of the Savitzky-Golay filter, a lowpass filter, peak detecting and clustering techniques to extract the heart rate from the vibration of the carotid artery and the jugular. We also propose an algorithm for detecting the apnea state of a monitored patient using the bispectral analysis. In our initial experiments, the proposed algorithms obtain positive achievements.

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References

  1. Caylak, E.: The genetics of sleep disorders in humans: narcolepsy, restless legs syndrome, and obstructive sleep apnea syndrome. Am. J. Med. Genet. Part A 149A, 2612–2626 (2009)

    Article  Google Scholar 

  2. Thuong, L., Phuc, N., Hoang, P., Tuan, V., Thien, L., Duc, N.: Accelerometer Sensor Model for Sleep Apnea Detection with Bispectral Signal Analysis. University of Technology Ho Chi Minh City, Vietnam, Dept. of Elec. and Electronics Eng (2019)

    Google Scholar 

  3. Sleep apnea - Symptoms and causes. https://www.mayoclinic.org/diseases-conditions/sleep-apnea/symptoms-causes/syc-20377631. Accessed 8 Jul 2019

  4. Institute for Quality and Efficiency in Health Care (IQWiG), Obstructive sleep apnea: Overview. https://www.ncbi.nlm.nih.gov/books/NBK279274/

  5. Javaheri, S., et al.: Sleep Apnea. J. Am. Coll. Cardiol. 69, 841–858 (2017)

    Article  Google Scholar 

  6. Surrel, G., Aminifar, A., Rincon, F., Murali, S., Atienza, D.: Online obstructive sleep apnea detection on medical wearable sensors. IEEE Trans. Biomed. Circuits Syst. 12, 762–773 (2018)

    Article  Google Scholar 

  7. Nam, Y., Kim, Y., Lee, J.: Sleep monitoring based on a tri-axial accelerometer and a pressure sensor. Sensors 16, 750 (2016)

    Article  Google Scholar 

  8. Penzel, T., McNames, J., de Chazal, P., Raymond, B., Murray, A., Moody, G.: Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med. Biol. Eng. Comput. 40, 402–407 (2002)

    Article  Google Scholar 

  9. CurioCity – CurioCité, Why does your heart rate decrease when you take a deep breath? http://explorecuriocity.org/Explore/ArticleId/705/why-does-your-heart-rate-decrease-when-you-take-a-deep-breath-705.aspx

  10. Yılmaz, B., Asyalı, M., Arıkan, E., Yetkin, S., Özgen, F.: Sleep stage and obstructive apneaic epoch classification using single-lead ECG. BioMedical Eng. OnLine 9, 39 (2010)

    Article  Google Scholar 

  11. Isa, S.M., Ivan Fanany, M., Jatmiko, W., Murni Arymurthy, A.: Sleep apnea detection from ECG signal: analysis on optimal features, principal components, and nonlinearity. In: 5th International Conference on Bioinformatics and Biomedical Engineering. IEEE (2011)

    Google Scholar 

  12. Chazal, P., Penzel, T., Heneghan, C.: Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram. Physiol. Meas. 25, 967–983 (2004)

    Article  Google Scholar 

  13. Kaguara, A., Nam, K.M., Reddy, S.: A deep neural network classifier for diagnosing sleep apnea from ECG data on smartphones and small embedded systems (2014)

    Google Scholar 

  14. Li, K., Pan, W., Li, Y., Jiang, Q., Liu, G.: A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal. Neurocomputing 294, 94–101 (2018)

    Article  Google Scholar 

  15. Penzel, T., Moody, G., Mark, R., Goldberger, A., Peter, J.: The apnea-ECG database. Comput. Cardiol. 27, 255–258 (2000)

    Google Scholar 

  16. Papini, G., et al.: On the generalizability of ECG-based obstructive sleep apnea monitoring: merits and limitations of the Apnea-ECG database. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2018)

    Google Scholar 

  17. Bucklin, C., Das, M., Luo, S.: An inexpensive accelerometer based sleep-apnea screening technique. In: IEEE 2010 National Aerospace & Electronics Conference, USA, pp 396–399 (2010)

    Google Scholar 

  18. Avalur, D.S.: Human Breath Detection using a Microphone. University of Groningen, Groningen (2013)

    Google Scholar 

  19. Jarchi, D., Rodgers, S., Tarassenko, L., Clifton, D.: Accelerometry-based estimation of respiratory rate for post-intensive care patient monitoring. IEEE Sens. J. 18, 4981–4989 (2018)

    Article  Google Scholar 

  20. Ruangsuwana, R., Velikic, G., Bocko, M.: Methods to extract respiration information from ECG Signals. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2010)

    Google Scholar 

  21. Pang, L., Tchoudovski, I., Braecklein, M., Egorouchkina, K., Kellermann, W., Bolz, A.: Real time heart ischemia detection in the smart home care system. In: Proceedings of IEEE on Engineering in Medicine and Biology Society (EMBS), vol. 886, pp. 3703–3706 (2005)

    Google Scholar 

  22. All About Heart Rate (Pulse). https://www.heart.org/en/health-topics/high-blood-pressure/the-facts-about-high-blood-pressure/all-about-heart-rate-pulse

  23. Acharya, U., Suri, J., Spaan, J.: Advances in Cardiac Signal Processing. Springer (2007)

    Google Scholar 

  24. Sahoo, G., Ari, S., Patra, S.: ECG signal analysis for detection of Heart Rate and Ischemic Episodes. ISSN 3, 2249–7277 (2013)

    Google Scholar 

  25. Walker, H., Hall, W., Hurst, J.: Clinical methods. Butterworths, Boston (1990)

    Google Scholar 

  26. Sharma, M., Agarwal, S., Acharya, U.: Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals. Comput. Biol. Med. 100, 100–113 (2018)

    Article  Google Scholar 

  27. Atri, R., Mohebbi, M.: Obstructive sleep apnea detection using spectrum and bispectrum analysis of single-lead ECG signal. Physiol. Meas. 36, 1963–1980 (2015)

    Article  Google Scholar 

  28. Janbakhshi, P., Shamsollahi, M.: Sleep apnea detection from single-lead ECG using features based on ECG-derived respiration (EDR) signals. IRBM 39, 206–218 (2018)

    Article  Google Scholar 

  29. Avcı, C., Akbaş, A.: Sleep apnea classification based on respiration signals by using ensemble methods. Bio-Med. Mater. Eng. 26, S1703–S1710 (2015)

    Article  Google Scholar 

  30. Aydoğan, O., Öter, A., Güney, K., Kıymık, M., Tuncel, D.: Automatic diagnosis of obstructive sleep apnea/hypopnea events using respiratory signals. J. Med. Syst. 40, 274 (2016)

    Google Scholar 

  31. Roche, F.: Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation AHA J. 100(13), 1411–1415 (1999)

    Article  Google Scholar 

  32. Savitzky, A., Golay, M.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964)

    Article  Google Scholar 

  33. Pandia, K., Ravindran, S., Cole, R., Kovacs, G., Giovangrandi, L.: Motion artifact cancellation to obtain heart sounds from a single chest-worn accelerometer. In: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE (2010)

    Google Scholar 

  34. Mendel, J.: Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proc. IEEE 79, 278–305 (1991)

    Article  Google Scholar 

  35. Nikias, C., Raghuveer, M.: Bispectrum estimation: a digital signal processing framework. Proc. IEEE 75, 869–891 (1987)

    Article  Google Scholar 

  36. Emin Tagluk, M., Sezgin, N.: A new approach for estimation of obstructive sleep apnea syndrome. Expert Syst. Appl. 38, 5346–5351 (2011)

    Article  Google Scholar 

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Acknowledgment

This research is partly funded by Ho Chi Minh City University of Technology under student research grants for the French-Vietnamese program.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Thuong Le-Tien, Thien Luong-Hoai, Minh Nguyen-Binh, Tuan Vu-Minh, Hoang Pham-Thai & Duc Nguyen-Huynh

  2. Institute for Biomedical Technology, University of Applied Science, Mannheim, Germany

    Phuc Nguyen

Authors
  1. Thuong Le-Tien
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  2. Phuc Nguyen
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  3. Thien Luong-Hoai
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  4. Minh Nguyen-Binh
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  5. Tuan Vu-Minh
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  6. Hoang Pham-Thai
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  7. Duc Nguyen-Huynh
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Corresponding authors

Correspondence to Thuong Le-Tien or Phuc Nguyen .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler Universität Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Telecommunications University, Nha Trang City, Vietnam

    Son Ha Bui

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Le-Tien, T. et al. (2019). A Combination Solution for Sleep Apnea and Heart Rate Detection Based on Accelerometer Tracking. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35652-1

  • Online ISBN: 978-3-030-35653-8

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