Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Adaptive Detection of Unusual Heartbeat According to R-wave Distortion on ECG Signal

심전도 신호에서 R파 왜곡에 따른 적응적 특이심박 검출

  • Lee, SeungMin (Graduate School of Electronics Engineering, Kyungpook National University) ;
  • Ryu, ChunHa (Graduate School of Electronics Engineering, Kyungpook National University) ;
  • Park, Kil-Houm (Graduate School of Electronics Engineering, Kyungpook National University)
  • Received : 2014.06.23
  • Accepted : 2014.08.28
  • Published : 2014.09.25
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Abstract

Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.

부정맥 심전도 신호는 전도장애 및 발생부위에 따라 특정 부위에서 비정상 모양을 띄는 특이심박을 포함하고 있다. 특이심박은 부정맥 등 다양한 질환을 진단 및 분류하는데 있어 유용하기 때문에 부정맥 심전도 신호에서 특이심박의 검출은 매우 중요하다. R-peak점에서의 전위, 첨도 및 R-R 간격은 심전도 신호가 R파에서 가지는 특성이다. 본 논문에서는 이를 바탕으로 특이심박 검출 방법을 제안한다. 제안한 방법은 특이심박이 확실할수록 특성값이 평균에서 크게 벗어난다는 점을 기반으로 평균과 표준편차를 이용하여 순차적으로 특이심박을 검출한다. MIT-BIH 부정맥 데이터베이스 중 R파 왜곡을 가지는 15개의 심전도 신호에 대해 기존의 고정된 문턱값을 사용한 검출 방법과 제안한 방법을 적용하여 특이심박을 검출하여 비교하였다. 실험을 통해 민감도를 약 50~70%에서 제안한 방법을 통해 97%로 크게 상향할 수 있었다.

Keywords

References

  1. M. Llamedo and J. Martinez, "Heartbeat classification using feature selection driven by database generalization criteria," IEEE Trans. Biomedical Engineering, vol. 58, no. 3, pp. 616-625, 2011. https://doi.org/10.1109/TBME.2010.2068048
  2. S. O. Kim, "Arrhythmia Detection Using Rhythm Features of ECG Signal," Journal of The Korea Society of Computer and Information, vol. 18, no. 8, pp. 131-139, 2013. https://doi.org/10.9708/jksci.2013.18.8.131
  3. R. J. Huszar, "Basic dysrhythmias: interpretation & management," Mosby, 2007.
  4. J. Pan and W. Tompkins, "A real-time QRS detection algorithm," IEEE Trans. Biomedical Engineering, vol. 32, no. 3, pp. 230-236, March 1985.
  5. D. S. Benitez, et al., "A new QRS Detection algorithm base on the hilbert transform," Computers in Cardiology of IEEE, vol. 27 pp. 379-382, 2000.
  6. C. Li, C. Zheng and C. tai, "Detection of ECG Characteristic points using wavelet transforms," IEEE Trans. Biomedical Engineering, vol. 32, pp. 21-28, 1995.
  7. J. J. Kim, J. S. Kim and K. H. Park, "R-wave Detection Algorithm in ECG Signal Using Adaptive Refractory Period," Journal of the Institute of Electronics Engineers of Korea, Vol. 50, No. 5, pp. 242-250, 2013. https://doi.org/10.5573/ieek.2013.50.5.242
  8. S. M. Lee, T. H. Kim and K. H. Park, "Sequential Defect Detection According to Defect Possibility in TFT-LCD Panel Image," Journal of the Institute of Electronics Engineers of Korea, Vol. 51, No. 4, pp. 123-130, 2014. https://doi.org/10.5573/ieie.2014.51.4.123
  9. K. H. Park, J. S. Kim, C. H. Ryu, B. J. Choi, and J. J. Kim, "Unusual Waveform Detection Algorithm in Arrhythmia ECG Signal," Journal of Korean Institute of Intelligent Systems, Vol. 23, No. 4, pp. 292-297, 2013. https://doi.org/10.5391/JKIIS.2013.23.4.292
  10. G. Moody and R. Mark, "The MIT-BIH arrhythmia database on CD-ROM and software for use with it," Computers in cardiology 1990, Proceedings, pp. 185-188, Chicago, USA, 1990.
  11. S. W. Kim, T. H. Kim, B. J. Choi and K. H. Park, "Minimizing Algorithm of Baseline Wander for ECG Signal using Morphology-pair," Journal of Korean Institute of Intelligent Systems, Vol. 20, No. 4, pp. 574-579, 2010. https://doi.org/10.5391/JKIIS.2010.20.4.574

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