Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Heart Rate Monitoring Using Motion Artifact Modeling with MISO Filters

MISO 필터 기반의 동잡음 모델링을 이용한 심박수 모니터링

  • Kim, Sunho (School of Electronic Engineering, Soongsil University) ;
  • Lee, Jungsub (School of Electronic Engineering, Soongsil University) ;
  • Kang, Hyunil (School of Electronic Engineering, Soongsil University) ;
  • Ohn, Baeksan (School of Electronic Engineering, Soongsil University) ;
  • Baek, Gyehyun (School of Electronic Engineering, Soongsil University) ;
  • Jung, Minkyu (School of Electronic Engineering, Soongsil University) ;
  • Im, Sungbin (School of Electronic Engineering, Soongsil University)
  • 김선호 (숭실대학교 정보통신공학과) ;
  • 이정섭 (숭실대학교 정보통신전자공학부) ;
  • 강현일 (숭실대학교 정보통신전자공학부) ;
  • 온백산 (숭실대학교 정보통신전자공학부) ;
  • 백계현 (숭실대학교 정보통신전자공학부) ;
  • 정민규 (숭실대학교 정보통신전자공학부) ;
  • 임성빈 (숭실대학교 전자정보공학부)
  • Received : 2015.06.24
  • Accepted : 2015.08.02
  • Published : 2015.08.25
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Abstract

Measuring the heart rate during exercise is important to properly control the amount of exercise. With the recent advent of smart device usage, there is a dramatic increase in interest in devices for the real-time measurement of the heart rate during exercise. During intensive exercise, accurate heart rate estimation from wrist-type photoplethysmography (PPG) signals is a very difficult problem due to motion artifact (MA). In this study, we propose an efficient algorithm for an accurate estimation of the heart rate from wrist-type PPG signals. For the twelve data sets, the proposed algorithm achieves the average absolute error of 1.38 beat per minute (BPM) and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.9922. The proposed algorithm presents the strengths in an accurate estimation together with a fast computation speed, which is attractive in application to wearable devices.

올바른 운동량 조절을 위해선 운동중의 심박수 측정이 중요하다. 최근 스마트 디바이스가 활발하게 사용됨에 따라, 운동중의 실시간 심박수 측정에 대한 관심이 급격하게 증가하고 있다. 고강도 운동 중에는 동잡음으로 인하여 손목 밴드 유형의 광혈류 (PPG : photoplethysmography) 측정기 신호로부터 정확한 심박수를 추정하는 것이 매우 어렵다. 본 논문에서는 손목밴드 유형의 광혈류 측정기 신호로부터 정확한 심박수 추정을 위한 효율적인 알고리즘을 제안하였다. 12개의 데이터 세트에 대하여 제안하는 알고리즘을 적용한 결과, 1.38의 분당심박수(BPM) 평균 절대 오차를 기록하였고, 0.9922의 추정 심박수와 실제 심박수간의 Pearson 상관계수를 얻었다. 제안하는 알고리즘은 웨어러블 디바이스에 적합한 빠른 연산속도와 정확한 추정을 가능케 한다.

Keywords

References

  1. J. Allen, "Photoplethysmography and its application in clinical physiological measurement," Physiol. Meas., vol. 28, no. 3, pp. R1-R39, 2007. https://doi.org/10.1088/0967-3334/28/3/R01
  2. Z. Zhang, Z. Pi, and B. Liu, "TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type PPG Signals During Intensive Physical Exercise," IEEE Trans. Biomed. Eng., vol. 62, no. 2, pp. 522-531, 2015. https://doi.org/10.1109/TBME.2014.2359372
  3. B. Kim and S. Yoo, "Motion artifact reduction in photoplethysmography using independent component analysis," IEEE Trans. Biomed. Eng., vol. 53, no. 3, pp. 566-568, Mar. 2006., May 1997. https://doi.org/10.1109/TBME.2005.869784
  4. B. Lee, J. Han, H. Baek, J. Shin, K. Park, and W. Yi, "Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry," Phys. Meas., vol. 31, no. 12, pp. 1585-1603, 2010. https://doi.org/10.1088/0967-3334/31/12/003
  5. S. Haykin, Adaptive Filter Theory, Pearson/Prentice Hall Upper Saddle River, NJ, 1996.
  6. R. Schmidt, "Multiple emitter Location and signal parameter estimation," IEEE Trans. Antennas and Propag., vol. 34, no. 3, pp. 276-280, 1986. https://doi.org/10.1109/TAP.1986.1143830
  7. P. Stoica and R. Moses, Spectral Analysis of Signals, Pearson/Prentice Hall Upper Saddle River, NJ, 2005.
  8. D. Altman and J. Bland, "Measurement in medicine: the analysis of method comparison studies." The Statistician, vol. 32, pp. 307-317, 1983. https://doi.org/10.2307/2987937

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  1. Characterization of Quadratic Nonlinearity between Motion Artifact and Acceleration Data and its Application to Heartbeat Rate Estimation vol.17, pp.8, 2017, https://doi.org/10.3390/s17081872