Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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2D ECG Compression Using Optimal Sorting Scheme

정렬과 평균 정규화를 이용한 2D ECG 신호 압축 방법

  • Lee, Kyu-Bong (Department of Electrical Engineering and Computer Science Kyungpook National University) ;
  • Joo, Young-Bok (Yonsei University) ;
  • Han, Chan-Ho (Department of Broadcasting Visual media and Technology Kangwon National University) ;
  • Huh, Kyung-Moo (Department of Electronics Engineering Dankook University) ;
  • Park, Kil-Houm (Department of Electrical Engineering and Computer Science Kyungpook National University)
  • 이규봉 (경북대학교 전자전기컴퓨터학부) ;
  • 주영복 (연세대학교) ;
  • 한찬호 (강원대학교 방송영상학부) ;
  • 허경무 (단국대학교 전자공학과) ;
  • 박길흠 (경북대학교 전자전기컴퓨터학부)
  • Published : 2009.07.25
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Abstract

In this paper, we propose an effective compression method for electrocardiogram (ECG) signals. 1-D ECG signals are reconstructed to 2-D ECG data by period and complexity sorting schemes with image compression techniques to increase inter and intra-beat correlation. The proposed method added block division and mean-period normalization techniques on top of conventional 2-D data ECG compression methods. JPEG 2000 is chosen for compression of 2-D ECG data. Standard MIT-BIH arrhythmia database is used for evaluation and experiment. The results show that the proposed method outperforms compared to the most recent literature especially in case of high compression rate.

이 논문에서는 효율적인 2D 방식의 심전도 신호 압축 방법을 제안한다. ID 심전도 신호는 2D 신호로 변환된 후 주기와 복잡도를 바탕으로 정렬되고 상호간의 상관 관계를 적용한다. 그 다음 불연속이 발생하는 지점을 기준으로 각 구간을 분할하고 주기의 평균으로 정규화 한 후 보통의 영상 신호를 압축하는 방식과 유사한 방식으로 정렬된 2D 신호를 압축한다. 압축 방식으로는 JPEG 2000이 사용되었으며 실험 데이터는 심전도 압축에서 표준화되어 사용되는 MIT-BIH arrhythmia database를 사용하였다. 제안된 방법은 기존의 2D 심전도 압축 방식과 비교하여 보다 개선된 성능을 보여 준다.

Keywords

References

  1. E.B.L. Filho, N.M.M. Rodrigues, E.A.B. da Silva, S.M.M. de Faria, V.M.M. da Silva, M.B. de Car-valho, 'ECG Signal Compression Based on Dc Equalization and Complexity Sorting,' IEEE Trans. Biomed. Eng., , Jul. 2008, vol. 55, pp. 1923 – 1926 https://doi.org/10.1109/TBME.2008.919880
  2. H.-H. Chou, Y.-J. Chen, Y.-C. Shiau, and T.-S. Kuo, 'An effective and efficient compression algorithm for ECG signals with irregular periods,' IEEE Trans. Biomed. Eng., Jun. 2006, vol. 53, no. 6, pp. 1198–1205 https://doi.org/10.1109/TBME.2005.863961
  3. S.-C. Tai, C.-C. Sun, and W.-C. Tan, '2-D ECG compression method based on wavelet transform and modified SPIHT,' IEEE Trans. Biomed. Eng., Jun. 2005, vol. 52, no. 6, pp. 999–1008 https://doi.org/10.1109/TBME.2005.846727
  4. A. Bilgin, M. W. Marcellin, and M. I. Altbach, 'Compression of electrocardiogram signals using JPEG2000,' IEEE Trans. Consum. Electron., Nov. 2003, vol. 49, no. 4, pp. 833–840 https://doi.org/10.1109/TCE.2003.1261162
  5. J.-J. Wei, C.-J. Chang, N.-K. Chou, and G.-J. Jan, 'ECG data compression using truncated singular value decomposition,' IEEE Trans. Inf. Technol. Biomed., Dec. 2001, vol. 5, no. 4, pp. 290–299 https://doi.org/10.1109/4233.966104
  6. H. Lee and K. M. Buckley, 'ECG data compression using cut and align beats approach and 2-D trans-forms,' IEEE Trans. Biomed. Eng., May 1999, vol. 46, no. 5, pp. 556–565 https://doi.org/10.1109/10.759056
  7. J. Cardenas-Barrera and J. Lorenzo-Ginori, 'Mean-shape vector quantization for ECG signal G. Nave, A. Cohen, 'ECG compression using long-term prediction,' IEEE Trans. Biomed. Eng., vol. 40, pp. 887–885, Sep. 1993 https://doi.org/10.1109/10.245608
  8. J. Cardenas-Barrera and J. Lorenzo-Ginori, 'Mean-shape vector quantization for ECG signal compression,' IEEE Trans. Biomed. Eng., Jan. 1999, vol. 46, no. 1, pp. 62–70 https://doi.org/10.1109/10.736756
  9. C.-C. Sun; S.-C. Tai, 'Beat-based ECG compression using gain-shape vector quantization,' IEEE Trans. Biomed. Eng., Nov. 2005, vol. 52, no. 11, pp. 1882–1888 https://doi.org/10.1109/TBME.2005.856270