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Baseline Wander Removing Method Based on Morphological Filter for Efficient QRS Detection

효율적인 QRS 검출을 위한 형태 연산 기반의 기저선 잡음 제거 기법

  • 조익성 (부산대학교 IT응용공학과) ;
  • 김주만 (부산대학교 IT응용공학과) ;
  • 김선종 (부산대학교 IT응용공학과) ;
  • 권혁숭 (부산대학교 IT응용공학과)
  • Received : 2012.08.22
  • Accepted : 2012.09.12
  • Published : 2013.01.31

Abstract

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. The important problem in recording ECG signal is a baseline wandering, which is occurred by rhythm of respiration and muscle contraction attaching to an electrode. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, baseline wander removing method based on morphological filter for efficient QRS detection method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. The signal distortion ratio of the proposed method is compared with other filtering method. Also, R wave detection is evaluated by using MIT-BIH arrhythmia database. Experiment result show that proposed method removes baseline wanders effectively without significant morphological distortion.

심전도 신호의 QRS 검출은 심장의 상태를 확인 할 수 있는 가장 보편적인 방법이다. 하지만 측정할 때 발생되는 여러 종류의 잡음성분들로 인하여 이를 분석하는데 어려움을 준다. 가장 큰 문제를 야기하는 부분이 기저선 변동 잡음인데 전극을 부착한 부위의 근육수축과 호흡의 리듬에 따라서 발생하게 된다. 특히 일반인들의 건강상태를 지속적으로 모니터링 해야 하는 헬스케어 시스템에서는 이를 위한 심전도 신호의 실시간 처리가 필요하다. 즉, 최소한의 연산량으로 대상 환자의 특징을 파악하여 정확한 QRS를 검출할 수 있는 적합한 알고리즘의 설계가 필요하다. 따라서 본 연구에서는 효율적인 QRS 검출을 위한 형태 연산기반의 기저선 잡음제거 기법을 제안한다. 이를 위해 형태 연산을 통한 전처리 과정과 적응형 윈도우를 통해 QRS를 검출하였다. 제안한 알고리즘의 성능을 평가하기 위해 일반적으로 심전도 기저선 변동 잡음 제거 시 사용되는 기존 필터와의 신호의 왜곡도를 비교 평가하였다. 또한 MIT-BIH 부정맥 데이터베이스를 사용하여 R파 검출 결과를 확인하였다. 실험 결과로부터 형태 연산을 이용한 방법이 적은 연산량으로 충분한 잡음제거율을 얻을 수 있다는 것을 확인할 수 있었다.

Keywords

References

  1. John G. Webster, "Encylclopedia of medical devices and instrumentation,"Wiley, pp20-28 1990
  2. Y. Suzuki, and K. Ono, "Personal computer system for ECG ST-segment recognition based on neural networks," Medical & Biological Engineering & Computer, Vol. 30, No. 1, pp.2-8, 1992. https://doi.org/10.1007/BF02446186
  3. Gary, M. F., Thomas, C. J., et al. "A coparison of the noise sensitivity of nine QRS Detection Algorithms", IEEE Trans. Biomed. Eng. 37(1), pp:85-98, 1990. https://doi.org/10.1109/10.43620
  4. Van Alste, J. A. and Schilder, T. S. "Removal of baseline wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps", IEEE trans. Biomed. Eng, BME-32(12), pp:1052-1060, 1985. https://doi.org/10.1109/TBME.1985.325514
  5. Van Alste, J. A. and Schilder, T. S. "Removal of baseline wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps", IEEE trans. Biomed. Eng, BME-32(12), pp:1052-1060, 1985. https://doi.org/10.1109/TBME.1985.325514
  6. Rodrigues, J., Olsson, L., Sornmo, T., & Owall, V., "Digital implementation of a wavelet-based event detector for cardiac pacemakers". IEEE TCAS-I, 52(12), 2686-2698. 2005
  7. Zhang, F., Wei, Y., & Lian, Y. . "Efficient QRS detection in wearable ECG Devices for body sensor network. The 5th IEEE International Workshop on Wearable and Implantable Body Sensor Networks", Hong Kong, China, Jun 1-3. 2008
  8. Zhang, F., Wei, Y., & Lian, Y. , "Frequency response masking based filter bank for qrs detection in wearable biomedical devices. IEEE International Symposiumon Circuits and Systems", Taipei, Taiwan, May 24-27. 2009
  9. Gasterators, A., Andreadis, I., & Tsalides, P. H. , "Fuzzy soft mathematical morphology". IEEE Proceedings Vision, Image and Signal Processing, 145, 41-49. 1998 https://doi.org/10.1049/ip-vis:19981557
  10. Maragos, P., Schafter, R. W., & Butt, M. A. . "Mathematical morphology and its applications to image and signal processing", by Kluwer Academic. 1996
  11. Chu, C.-H. N. & Delp, E. J. . "Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators". IEEE Transactions on Biomedical Engineering, 36, 262-273. 1989 https://doi.org/10.1109/10.16474
  12. Trahanias, P. E. . "An approach to QRS complex detection using mathematical morphology". IEEE Transactions on Biomedical Engineering, 40, 201- 205. 1993 https://doi.org/10.1109/10.212060

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