한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

  • 제37권4C호
  • /
  • Pages.313-320
  • /
  • 2012
  • /
  • 1226-4717(pISSN)
  • /
  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지
DOI QR코드

DOI QR Code

PQRST파 특징 기반 신호의 분류를 이용한 심전도 압축 알고리즘 성능 평가

Performance Evaluation of ECG Compression Algorithms using Classification of Signals based PQSRT Wave Features

  • 구정주 (조선대학교 정보통신공학과 SoC설계실습실) ;
  • 최광석 (조선대학교 정보통신공학과)
  • 투고 : 2012.02.29
  • 심사 : 2012.04.10
  • 발행 : 2012.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

심전도의 압축은 시스템의 처리 속도를 높일 뿐만 아니라 신호의 전송량, 장기적인 기록 데이터 저장량을 줄일 수 있다. 본 논문에서는 기존의 심전도 데이터의 손실 혹은 무 손실 압축 알고리즘에 대한 성능 평가가 엔지니어의 관점에서 PRD(Percent RMS Difference)와 CR(Compression Ratio)을 측정하였다면 심전도를 진단하는 진단자의 관점에서 압축의 성능 평가에 대한 연구를 하였다. 일반적으로 심전도 데이터의 압축이 진단에 영향을 미치지 않게 하기위해서는 압축 후 복원된 PQRST파의 위치, 길이, 진폭, 파의 형태 등 진단에 필요한 것들이 손상되어선 안 된다. 대표적인 심전도 압축 알고리즘 AZTEC은 기존의 성능평가에 그 효율성이 검증되었지만 진단자의 관점에서 새로운 성능평가를 제시한다.

An ECG(Electrocardiogram) compression can increase the processing speed of system as well as reduce amount of signal transmission and data storage of long-term records. Whereas conventional performance evaluations of loss or lossless compression algorithms measure PRD(Percent RMS Difference) and CR(Compression Ratio) in the viewpoint of engineers, this paper focused on the performance evaluations of compression algorithms in the viewpoint of diagnostician who diagnosis ECG. Generally, for not effecting the diagnosis in the ECG compression, the position, length, amplitude and waveform of the restored signal of PQRST wave should not be damaged. AZTEC, a typical ECG compression algorithm, is validated its effectiveness in conventional performance evaluation. In this paper, we propose novel performance evaluation of AZTEC in the viewpoint of diagnostician.

키워드

참고문헌

  1. Dale Davis. "Quick and Accurate 12-Lead ECG Interpretation", 군자, pp.55-66
  2. S.-j. Lee and M. Lee, "A real-time ECG data compression algorithm for a digital Holter system," in Proc. IEEE 30th Annu. Int. Conf. Eng. Med. Biol. Soc., Aug. 20-25, 2008, pp. 4736-4739.
  3. Y. Zigel, A. Cohen, and A. Katz, "ECG signal compression using analysis by synthesis coding," IEEE Trans. Biomed. Eng., vol. 47, no. 10, pp. 1308-1316, Oct. 2000. https://doi.org/10.1109/10.871403
  4. Kim, H., R. F. Yazicioglu, et al, "ECG signal compression and classification algorithm with quad level vector for ECG holter system Information Technology in Biomedicine," IEEE Transactions on 14(1): 93-100, 2010.
  5. B. Furht and A.Perez, "AN adaptive real-time ECG compression algorithm with variable threshold," IEEE Trans. Biomed. Eng., vol. 35, pp. 489-494, June 1988. https://doi.org/10.1109/10.2121
  6. R. C. Barr, S. M. Blanchard, and D. A. Dipersio, "SAPA-2 is the Fan," IEEE Trans. Biomed. Eng., vol. BME-32, no. 5, pp. 337, May 1985.
  7. M. E. Womble, J. S. Halliday, S. K. Mitter, M. C. Lancaster, J. H. Triebwasser, "Data Compression for Storing and Transmitting ECG's / VCG's", Proceedings of the IEEE, Vol. 65, No5, May 1977, pp. 703-706.
  8. V. A. Allen, J. Belina J., "ECG data compression using the discrete cosine transform", Proc. of Computers in Cardiology, IEEE Computer Society Press, Silverspring, MD, 1992, pp. 687-690.
  9. Z. Lu., D. Y. Kim, and W. A. Pearlman, "Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm," IEEE Transactions on Biomedical Engineering, vol.47, no.7, pp. 849-856, July 2000. https://doi.org/10.1109/10.846678
  10. Sang-il Ko, Jong-suk Choi and Byoung-hoon Kim, A. G. Ramakrishnan and S. Saha, "ECG coding by wavelet-based linear prediction," IEEE Trans. Biomed. Eng., vol. 44, pp. 394-402, May 1997. https://doi.org/10.1109/10.568915
  11. Han MS, Ahn HM, Lee JM, Kim YK, "A Study on Hybrid ECG Compression Algorithm Using Templetes and AZTEC," 대한의료정보학회지, 제6권, 제3호, vol. 6, no. 3. 2000.
  12. Tae-Hun Kim, Sung-Wan Kim, Chun-Ha Ryu, Byoung-Ju Yun, jeong-Hong Kim, Byung-Jae Choi and Kil-Houm Park, "ECG Signal Compression usting Feature Point based on Curvature" 한국지능시스템학회 논문지, vol. 20, no. 5, pp. 624-630, 2010.