한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제21권7호
  • /
  • Pages.1420-1428
  • /
  • 2017
  • /
  • 2234-4772(pISSN)
  • /
  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

심전도신호 샘플링 주파수에 따른 R파 검출 최적 문턱치 설정

Optimal Threshold Setting Method for R Wave Detection According to The Sampling Frequency of ECG Signals

  • Cho, Ik-sung (Department of Information and Communication Engineering, Kyungwoon University) ;
  • Kwon, Hyeog-soong (Department of IT Engineering, Pusan National University)
  • 투고 : 2017.03.09
  • 심사 : 2017.05.11
  • 발행 : 2017.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

R파 검출에 사용되는 여러 심전도 데이터베이스는 샘플링 주파수의 차이로 인해 서로 다른 환경에 적용할 경우 성능에 변화가 많아 알고리즘의 신뢰도를 보장하기 어렵다. 본 연구에서는 심전도신호의 샘플링 주파수에 따른 R파 검출의 최적 문턱치 설정 방법을 제안한다. 이를 위해 미분 기반의 이동평균과 제곱합수를 이용하여 전처리를 수행하였다. 이후 샘플링 주파수에 따라 피크 문턱치에 대한 최적 값을 검출하였다. 문턱치 단계는 신호의 변화와 이전 검출된 피크 값에 따라 문턱치를 변경함으로써 최적의 성능을 나타내는 값을 선정하는 과정으로 실험하였다. 제안한 방법의 우수성을 입증하기 위해 부정맥 데이터베이스 레코드를 대상으로 실험한 결과 MIT-BIH 샘플링 주파수 360Hz에 대한 미분 구간($N_d$), 윈도우 사이즈(N), 문턱 계수($p_{th}$)의 최적 값은 각각 7, 8, 6.6일 때 R파 검출율은 99.758%의 우수한 성능을 나타내었다.

It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.

키워드

참고문헌

  1. S.-H. Liou, Y.-H. Wu, Y.-S. Syu, Y.-L. Gong, H.-C. Chen, and S.-T. Pan, "Real-time remote ECG signal monitor and emergency warning/positioning system on cellular phone," Intelligent Information and Database Systems, vol. 7198, Berlin, Germany: Springer-Verlag, pp. 336-345, 2012.
  2. P. E. Dilaveris, et al, "Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation," American. Heart Journal, vol. 135, pp. 733- 738, May 1998. https://doi.org/10.1016/S0002-8703(98)70030-4
  3. M. G. Tsipouras, D.I. Fotiadis, D. Sideris, "Arrhythmia classification using the RR-interval duration signal," In Proceedings of 2002 Computers in Cardiology, pp. 485- 488, Sept. 2002.
  4. M. J. Rooijakkers et al., "Feasibility Study of a New Method for Low-Complexity Fetal Movement Detection From Abdominal ECG Recordings," IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 5, pp. 1361 - 1368, Sept. 2016. https://doi.org/10.1109/JBHI.2015.2452266
  5. Y.-L. Zheng et al., "Unobtrusive sensing and wearable devices for health informatics," IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1538-1554, May 2014. https://doi.org/10.1109/TBME.2014.2309951
  6. S. Pal and M. Mitra, "Empirical mode decomposition based ECG enhancement and QRS detection," Computers in Biology and Medicine, vol. 42, no. 1, pp. 83-92, Jan. 2012. https://doi.org/10.1016/j.compbiomed.2011.10.012
  7. P. Li, Y. Wang et al., "High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal," IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 78 - 86, Jan. 2017. https://doi.org/10.1109/TBME.2016.2539421
  8. C. J. Deepu, X. Zhang, C. H. Heng, Y. Lian, "A 3-Lead ECG-on-Chip with QRS Detection and Lossless Compression for Wireless Sensors," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 63, no. 12, pp. 1151-1155, Dec. 2016. https://doi.org/10.1109/TCSII.2016.2613564
  9. L. Zapanta, C. Poon, D. White, C. Marcus, E. Kaltz, "Heart rate chaos in obstructive sleep apnea in children," In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS''04), San Francisco, CA, USA, vol. 1-5, pp. 3889- 3892, Sep. 2004.
  10. I. S. Cho, H. S.Kwon, J.O. Yun, "Detection of QRS Feature Based on Phase Transition Tracking for Premature Ventricular Contraction Classification," Journal of Korea Institute of Information and Communication Engineering, vol. 20, no. 2, pp. 427-436, February 2016. https://doi.org/10.6109/jkiice.2016.20.2.427
  11. H. Roskamm, G. Csapo, Disorders of Cardiac Function, New York, Marcel Dekker, 1982.
  12. A. Gacek, W. Pedrcyz, ECG Signal processing, classification and interpretation, London, Springer, 2012.
  13. T. Azeem, M. Vassallo, and N. J. Samani, Rapid Review of ECG Interpretation, Boca Raton, FL: Manson Publishing, 2005.
  14. S. W. Chen, H. C. Chen, H. L. Chan, "A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising," Computer Methods and Programs in Biomedicine Journal, vol. 82, no. 3, pp.187-195, Jun. 2006. https://doi.org/10.1016/j.cmpb.2005.11.012
  15. H. Zheng and J. Wu, "Real-time QRS detection method," In Proceedings of 10th International Conference on e-health Networking, Applications and Services(HealthCom 2008), pp. 169-170, July 2008.
  16. J. Pan and W. J. Tompkins, "A real-time QRS detection algorithm," IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230-236, Mar. 1985.
  17. J. P. Martínez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, "A wavelet-based ECG delineator: Evaluation on standard databases," IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, pp. 570-581, April 2004. https://doi.org/10.1109/TBME.2003.821031

피인용 문헌

  1. 심실 조기 수축 비트 검출을 위한 딥러닝 기반의 최적 파라미터 검출 vol.23, pp.12, 2019, https://doi.org/10.6109/jkiice.2019.23.12.1542
  2. AR 기반의 특징점 추출과 딥러닝을 통한 부정맥 분류 vol.24, pp.10, 2017, https://doi.org/10.6109/jkiice.2020.24.10.1341