DOI QR코드

DOI QR Code

Design of Motion Artifacts Filter of PPG Signal based on Kalman filter and Adaptive filter

칼만필터와 적응필터를 기반한 PPG 동잡음 제거 필터 설계

  • Lee, Byeong-Ro (Department of Electronics Engineering, Gyeongnam Jinju National University of Science and Technology) ;
  • Lee, Ju-Won (Departtment of Medical Engineering, Andong Science College)
  • Received : 2014.02.14
  • Accepted : 2014.03.11
  • Published : 2014.04.30

Abstract

The PPG signal used in mobile-healthcare and telemedicine system is including the various motion artifact that is signal generated from patient's movements. Recently, although the various methods to remove motion artifacts have been suggested, the performances of these methods are still not satisfactory. Therefore, this s study suggested the novel method based on the Kalman filter and adaptive filter to remove motion artifacts, and we used various motion artifacts to analyze the performance of the proposed method. In the results of experiments, the signal-to-noise ratio of proposed method showed good performace that was 4.8 times of moving average filter.

모바일 헬스케어와 원격진료에서 사용되는 광용적 맥파(PPG) 신호는 환자로부터 발생되는 여러 가지의 동잡음을 포함하고 있다. 이러한 동잡음을 제거하기위해 이동평균필터, 적응필터, 가속도 센서 등을 이용한 기법이 제시되었으나, 아직까지도 그 성능이 만족 스럽지 못하다. 따라서 본 연구에서는 이러한 동잡음 문제를 해결하기 위해 칼만필터와 적응필터를 이용한 새로운 동잡음 제거 기법을 제안하고 그 성능을 평가하기위해 다양한 동잡음을 사용하였다. 이 실험의 결과에서 제안된 방법의 신호대 잡음비는 이동 평균 필터 보다 4.8배인 우수한 성능을 보였다.

Keywords

References

  1. Yoon, G., Lee, J. Y., Jeon, K. J., et all, "Development of a compact home health monitor for telemedicine", Telemed. J. E. Health, vol. 11, no. 6, pp. 660-667, January, 2005. https://doi.org/10.1089/tmj.2005.11.660
  2. M. Folke, L. Cernerud, M. Ekstrom, B. Hok, "Critical review of non-invasive respiratory monitoring in medical care", Medical & Biological Engineering & Computing, Vol. 41, Issue 4, pp.377-383, July, 2003. https://doi.org/10.1007/BF02348078
  3. J. Muhlsteff, O. Such, R. Schmidt, M. Perkuhn, H. Reiter, et al, "Wearable approach for continuous ECG and activity patient-monitoring", Engineering in Medicine and Biology Society, 26th Annual International Conference of the IEEE, vol. 1, no. 1-5, pp. 2184 -2187, Sept. 2004.
  4. I. Brown and A. A. Adams, "The ethical challenges of ubiquitous healthcare", International Review of Information Ethics, vol. 8, no. 12, pp. 53-60, 2007.
  5. J. W. Lee, W. G. Jung, G. K. Lee, et all, "Design of filter to reject motion artifact of pulse oximetry", Computer Standards & Interfaces, vol. 26, no. 3, pp. 241-249, May, 2004. https://doi.org/10.1016/S0920-5489(03)00077-1
  6. Ju-Won Lee, Jae-Hyun Nam, "Design of Filter to Reject Motion Artifacts of PPG Signal by Using Two Photosensors", J. lnf. Commun. Converg. Eng. vol. 10, No. 1, pp. 91-95, Mar. 2012.
  7. Wan-Young Chung, S. Bhardwaj, A. Purwar, Dae-Seok Lee, R. Myllylae, "A Fusion Health Monitoring Using ECG and Accelerometer sensors for Elderly Persons at Home", Proceeding of Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE, Issue 22-26, pp. 3818-3821, Aug, 2007.
  8. Ram, M.R., Madhav, K,V., Krishna ,E.H., "A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter", IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 5, pp. 1445-1457, May, 2012. https://doi.org/10.1109/TIM.2011.2175832
  9. Boreom Lee, Jonghee Han, Hyun Jae Baek, Jae Hyuk Shin, Kwang Suk Park, Won Jin Yi, "Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry", Physiological Measurement, vol. 31, no. 12, pp.1585-1603, October, 2010. https://doi.org/10.1088/0967-3334/31/12/003
  10. Han-Wook Lee, Ju-Won Lee, Won-Geun Jung, and Gun-Ki Lee, "The Periodic Moving Average Filter for Removing Motion Artifacts from PPG Signals", International Journal of Control, Automation, and Systems, vol. 5, no. 6, pp. 701-706, December, 2007.
  11. Juwon Lee, "Design of Kalman Filter to Estimate Heart Rate Variability from PPG Signal for Mobile Healthcare", Journal of information and communication convergence engineering, vol. 8, no. 2, pp. 201-204, April 2010. https://doi.org/10.6109/jicce.2010.8.2.201
  12. Shifei Yuan, Hongjie Wu, Chengliang Yin, "State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model", Energies, vol. 6, no. 1, pp.444-470, Jan. 2013. https://doi.org/10.3390/en6010444
  13. Dan Simon, "Kalman Filtering", Embedded Systems Programming, pp. 72- 79, June 2001.
  14. Simon O. Haykin, "Adaptive Filter Theory", Prentice Hall, pp. 365-372, 2002.

Cited by

  1. 유사 주기성을 이용한 PFS 펄스 신호의 동잡음 제거 vol.9, pp.6, 2014, https://doi.org/10.17661/jkiiect.2016.9.6.591
  2. 가압식 비침습적 대뇌 혈류 증가 장치의 구현 vol.21, pp.9, 2014, https://doi.org/10.6109/jkiice.2017.21.9.1752
  3. 혈류지수를 이용한 비침습적 대뇌혈류증가 장치의 구현 vol.21, pp.9, 2014, https://doi.org/10.6109/jkiice.2017.21.9.1761
  4. Blood Oxygen Level Sensor를 이용한 대뇌혈류증가 장치 vol.22, pp.8, 2014, https://doi.org/10.6109/jkiice.2018.22.8.1083
  5. Detection of Motion Artifact in PPG Signal using Convolutional Neural Network vol.20, pp.2, 2014, https://doi.org/10.9728/dcs.2019.20.2.355