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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Atrial Fibrillation Detection Algorithm through Non-Linear Analysis of Irregular RR Interval Rhythm

불규칙 RR 간격 리듬의 비선형적 특성 분석을 통한 심방세동 검출 알고리즘

  • 조익성 (부산대학교 IT응용공학과) ;
  • 권혁숭 (부산대학교 IT응용공학과)
  • Received : 2011.07.17
  • Accepted : 2011.10.10
  • Published : 2011.12.31
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Abstract

Several algorithms have been developed to detect AF which rely either on the form of P waves or the based on the time frequency domain analysis of RR variability. However, locating the P wave fiducial point is very difficult because of the low amplitude of the P wave and the corruption by noise. Also, the time frequency domain analysis of RR variability has disadvantage to get the details of irregular RR interval rhythm. In this study, we describe an atrial fibrillation detection algorithm through non-linear analysis of irregular RR interval rhythm based on the variability, randomness and complexity. We employ a new statistical techniques root mean squares of successive differences(RMSSD), turning points ratio(TPR) and sample entropy(SpEn). The detection algorithm was tested using the optimal threshold on two databases, namely the MIT-BIH Atrial Fibrillation Database and the Arrhythmia Database. We have achieved a high sensitivity(Se:94.5%), specificity(Sp:96.2%) and Se(89.8%), Sp(89.62%) respectively.

지금까지 심방세동을 검출하는 방법은 P파의 형태, 시간 주파수 영역 분석법이 주를 이루었다. 하지만 P파는 잡음의 영향을 많이 받는 환경에서는 검출의 정확도가 떨어지며, 시간 주파수 영역 분석법은 RR 간격에 따라 변화하는 불규칙적 리듬에 관한 정보를 정확하게 얻지 못하는 단점이 있다. 본 연구에서는, P파의 형태는 고려하지 않고, 불규칙 RR 간격 리듬의 비선형적 특성 분석을 통한 심방세동 검출 알고리즘을 제안한다. 이를 위해 불규칙 RR 간격 리듬을 다양성, 무작위성, 복잡성으로 각각 정의하고 제곱평균제곱근(RMSSD), 전환점비(TPR), 표본 엔트로비(SpEn)의 3가지 비선형적 특성 분석을 통하여 심방세동을 분류하였다. 제안된 알고리즘의 검출 성능을 평가하기 위해 3가지 통계치의 최적값을 설정하고 MIT-BIH 심방세동 데이터베이스와 부정맥 데이터베이스를 이용하여 실험하였다. 성능 평가 결과, MIT-BIH 심방세동 데이터베이스에 대해서는 민감도(sensitivity:94.5%), 특이도(specificity:96.2%)를 각각 나타내었으며, 부정맥 데이터베이스에 대해서는 민감도(89.8%), 특이도(89.62%)를 각각 나타내었다.

Keywords

References

  1. A. Bollmann, F. Lombardi, Electrocardiology of Atrial Fibrillation. Current Knowledge and Future Challenges, IEEE Engineering in Medicine and Biology Magazine, Vol. 25, No. 6, pp. 15-23, 2006.
  2. C. Furberg, B. Psaty, T. Manolio, J. Gardin, V. Smith, P. Rautaharju, Prevalence of atrial fibrillation in elderly subjects, The American Journal of Cardiology, Vol. 74, Issue 3, pp 236-241, 1994. https://doi.org/10.1016/0002-9149(94)90363-8
  3. Heeringa, J., D. A. van der Kuip, A. Hofman, J. A. Kors, G. van Herpen, B. H. Stricker, T. Stijnen, G. Y. Lip, and J. C. Witteman. Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study. Eur. Heart J. 27: 949-953, 2006. https://doi.org/10.1093/eurheartj/ehi825
  4. Benjamin, E. J., P. A. Wolf, R. B. D'Agostino, H. Silbershatz, W. B. Kannel, and D. Levy. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation 98:946-952, 1998. https://doi.org/10.1161/01.CIR.98.10.946
  5. Wolf, P. A., R. D. Abbott, and W. B. Kannel. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke 22:983-988, 1991. https://doi.org/10.1161/01.STR.22.8.983
  6. Aytemir, K., S. Aksoyek, A. Yildirir, N. Ozer, and A. Oto. Prediction of atrial fibrillation recurrence after cardioversion by P wave signal-averaged electrocardiography. Int. J. Cardiol. 70:15-21, 1999. https://doi.org/10.1016/S0167-5273(99)00038-8
  7. Clavier, L., J. M. Boucher, R. Lepage, J. J. Blanc, and J. C. Cornily. Automatic P-wave analysis of patients prone to atrial fibrillation. Med. Biol. Eng. Comput. 40:63-71, 2002. https://doi.org/10.1007/BF02347697
  8. Dotsinsky, I. Atrial wave detection algorithm for discovery of some rhythm abnormalities. Physiol. Meas. 28:595-610, 2007. https://doi.org/10.1088/0967-3334/28/5/012
  9. Duverney, D., J. Gaspoz, V. Pichot, F. Roche, R. Brion, A. Antoniadis, and J. Barthe' le'my. High accuracy of automatic detection of atrial fibrillation using wavelet transform of heart rate intervals. Pacing Clin. Electrophysiol. 25:457-462, 2002. https://doi.org/10.1046/j.1460-9592.2002.00457.x
  10. Sarkar, S., D. Ritscher, and R. Mehra. A detector for a chronic implantable atrial tachyarrhythmia monitor. IEEE Trans. Biomed. Eng. 55:1219-1224, 2008. https://doi.org/10.1109/TBME.2007.903707
  11. Tateno, K., and L. Glass. A method for detection of atrial fibrillation using RR intervals. Comput. Cardiol. 27:391-394, 2000.
  12. Tateno, K., and L. Glass. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and DRR intervals. Med. Biol. Eng. Comput. 39:664-671, 2001. https://doi.org/10.1007/BF02345439
  13. 조익성, 권혁숭, "PVC 분류를 위한 적응형 문턱치와 윈도우 기반의 R파 검출 알고리즘," 한국통신학회 논문지 제34권 11호, pp. 1289-1295. 2009년 11월.
  14. Saul JP (1990) Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. News Physiol Sci 5:32-37.
  15. S. Dash, K. H. Chon, S. Lu and E. A. Raeder "Automatic Real Time Detection of Atrial Fibrillation," Annals of Biomedical Engineering, Vol. 37, No. 9, pp. 1701-1709 (2009). https://doi.org/10.1007/s10439-009-9740-z
  16. J.S. Richman, J.R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. Heart Circ. Physiol. 278(6) (2000) H2039-H2049.