Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Study on the Holter Data Compression Algorithm -Using Piecewise Self-Affine Fractal Model-

Holter Data 압축 알고리즘에 관한 연구 -Piecewise Self-Affine Fractal Model을 이용한-

  • 전영일 (연세대학교 보건과학대학 의용전자공학과) ;
  • 정형만 (연세대학교 공과대학 전자공하과, 연세대학교 보건과학대학 의용전자공학과)
  • Published : 1995.03.01
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Abstract

This paper presents a new compression method (or ECG data using iterated contractive transformations. The method represents any range of ECG signal by piecewise self-afrine fractal Interpolation (PSAFI). The piecewise self-afrine rractal model is used where a discrete data set is viewed as being composed of contractive arfine transformation of pieces of itself. This algorithm was evaluated using MIT/BIH arrhythmia database. PSAFI is found to yield a relatively low reconstruction error for a given compression ratio than conventional direct compression methods. The compression ratio achieved was 883.9 bits per second (bps) - an average percent rms difference (AFRD) of 5.39 percent -with the original 12b ECG samples digitized at 400 Hz.

본 논문은 iterated contractive transformations을 이용한 심전도 데이터 압축에 관한 새로운 방법을 제안한다. 이방법은 piecewise self-affine fractal interpolation(PSAFI)에 의해 심전도 신호의 임의 구간들을 표현한다. Piecewise self-affine fractal model은 자기자신의 수축적 유사 변환으로 구성된다고 볼 수 있는 이산 데이터에 사용된다. 제안된 알고리즘은 MIT/BIH arrhythmia 데이터베이스로 평가되었다. PSAFI는 주어진 압축율에서 기존의 직접 압축 방법보다 상대적으로 적은 재생 오차를 나타냈다. 샘플링 주파수는 400Hz이고 resolution은 12bits인 원래 신호에 대해 압축율이 883.9bps일때 평균재생오차(APRD)는 5.39%를 나타냈다.

Keywords

References

  1. IEEE Trans. Biomed. Eng. v.BME-40 ECG compression using long-term prediction G.Nave;A.Cohen
  2. Biomed. Sic. Inst. v.14 Arrhythmia detection software for an ambulatory ECG monitor W.C.Mueller
  3. IEEE Trans. Biomed. Eng. v.BME-15 AZTEC a preprocessing program for real-time ECG rhythm analysis J.R.Cox;F.M.Nolle;H.A.Fozzard;G.C.Oliver
  4. IEEE Trans. Biomed. Eng. v.BME-35 An adaptive real time ECG compression algorithm with variable threshold B.Furht;A.Perez
  5. IEEE Trans. Biomed. Eng. v.BME-29 New data-reduction algorithm for real-time ECG analysis J.P.Abenstein;W.J.Tompkins
  6. Med. Biol. Eng. Comput. v.23 Comparison of methods for adaptive sampling of cardiac electrograms and electrocardiograms S.M.Blanchard;R.C.Barr
  7. IEEE Trans. Biomed. Eng. v.BME-33 ECG data compression using Fourier descriptors B.R.Shankara;I.S.N.Murthy
  8. Proc. IEEE v.65 Data compression for storing and transmitting ECG's/VCG's M.E.Womble;J.S.Halliday;S.K.Mitter;M.C.Lancaster;J.H.Triebwasser
  9. IEEE Trans. Biomed. Eng. v.BME-26 Compression of the ECG by prediction on interpolation and entropy encoading U.E.Rattimann;H.Y.Pipberger
  10. IEEE Eng. Med. Biolo. Mag. Data compression o the ECG using neural network for digital Holter monitor A.Iwata;Y.Nagasaka;N.Suzumura
  11. IEEE Trans. Biomed. Eng. v.BME-37 ECG data compression techniques-A unified approach S.Jalaleddine;C.Hutchens;R.Strattan;W.Coberly
  12. IEEE Trans. Image Processing v.1 no.1 Image coding based on a fractal theory o iterated contractive image transfomations A.E.Jacquin
  13. 전자공학회지 v.31-B no.2 블럭단위의 플렉탈 근사화를 이용한 영상코딩 정현민;김용규;윤택현;강션철;이병래;박규태
  14. Fractals Everywhere M.F.Barnsley
  15. NOSC TR-1362 Fractal-Based Image Compression Ⅱ E.W.Jacobs;R.D.Boss;Y.Fisher
  16. Signal Processing v.29 no.13 Image Compression: A study of the iterated transform method E.W.Jacobs;Y.Fisher;R.D.Boss
  17. IEEE Trans. Signal Processing v.40 no.7 Using iterated function systems to model discrete sequences D.S.Mazel;M.H.Hayes
  18. Med. Biol. Eng. Comput. v.30 Ventricular beat classifier using fractal number clustering H.Bakardjian
  19. 의공학회지 v.11 no.1 심전도 데이타 압축 알고리즘의 성능 개선에 관한 연구 이병채;황선철;이명호
  20. 대한 의공학회 춘계학술대회 논문집 v.16 no.1 반복 함수계를 이용한 심전도 데이터 압축 전영일;이순혁;이지연;윤영로;윤형로