Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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A probabilistic knowledge model for analyzing heart rate variability

심박수변이도 분석을 위한 확률적 지식기반 모형

  • 손창식 (DGIST 웰니스융합연구센터) ;
  • 강원석 (DGIST 웰니스융합연구센터) ;
  • 최락현 (DGIST 웰니스융합연구센터) ;
  • 박형섭 (계명대학교 의과대학교 내과학교실) ;
  • 한성욱 (계명대학교 동산의료원 심장내과) ;
  • 김윤년 (계명대학교 의과대학교 내과학교실)
  • Received : 2015.06.04
  • Accepted : 2015.06.30
  • Published : 2015.06.30
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Abstract

This study presents a probabilistic knowledge discovery method to interpret heart rate variability (HRV) based on time and frequency domain indexes, extracted using discrete wavelet transform. The knowledge induction algorithm was composed of two phases: rule generation and rule estimation. Firstly, a rule generation converts numerical attributes to intervals using ROC curve analysis and constructs a reduced ruleset by comparing consistency degree between attribute-value pairs with different decision values. Then, we estimated three measures such as rule support, confidence, and coverage to a probabilistic interpretation for each rule. To show the effectiveness of proposed model, we evaluated the statistical discriminant power of five rules (3 for atrial fibrillation, 1 for normal sinus rhythm, and 1 for both atrial fibrillation and normal sinus rhythm) generated using a data (n=58) collected from 1 channel wireless holter electrocardiogram (ECG), i.e., HeartCall$^{(R)}$, U-Heart Inc. The experimental result showed the performance of approximately 0.93 (93%) in terms of accuracy, sensitivity, specificity, and AUC measures, respectively.

본 논문에서는 이산 웨이블릿 변환을 통해 추출된 시간 영역과 주파수 영역의 특징들을 활용하여 심박수변이도를 확률적인 지식으로 분석할 수 있는 방법을 제안하였다. 제안된 방법에서 지식획득 알고리즘은 규칙생성과 규칙평가 단계로 구성되어 있으며, 규칙생성에서는 ROC 분석을 통해 수치적인 속성값을 이산화된 구간으로 변환하고, 서로 다른 의사결정값을 포함하는 구간들 사이에 일관성 정도를 비교함으로써 감축된 규칙-집합을 생성한다. 이때 규칙-집합 내에 각 규칙에 대해서 확률적 해석을 위한 3가지 척도를 추정하였다. 제안된 모형의 효과성은 심혈관질환 병력을 가진 58명의 심전도 데이터로부터 심방세동을 식별할 수 있는 5가지 규칙을 생성하였고, 이들 규칙의 분별력을 평가하였다. 실험결과, 제안된 모형으로부터 생성된 지식은 4가지 성능평가 척도에 대해서 각각 93%의 정확도를 보여주었다.

Keywords

References

  1. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, "Heart rate variability: standards of measurement, physiological interpretation and clinical use." Circulation, Vol. 93, pp. 1043-1065, 1996. https://doi.org/10.1161/01.CIR.93.5.1043
  2. M. T. La Rovere, J. T. Bigger, F. I. Marcus, "Baroreflex sensitivity and heart rate variability in prediction of total cardiac mortality after myocardial infarction," Lancet, Vol. 14, pp. 478-484, 1998.
  3. J. T. Bigger, J. L. Fleiss, L. M. Rolintzky, R. C. Steinman, "Frequency domain measures of heart period variability to assess risk late after myocardial infarction," Journal of the American College of Cardiology, Vol. 27, pp. 729-736, 1993.
  4. L. Forslund, I. Bjokander, M. Ericson, C. Held, T. Kahan, N. Rehnqvist, P. Hjemdahl, "Prognostic implications of autonomic function assessed by analyses of catecholamines and heart rate variability in stable angina pectoris," Heart, Vol. 87, pp. 415-422, 2002. https://doi.org/10.1136/heart.87.5.415
  5. M. K. Kim, D. G. Shin, Y. H. Park, J. H. Suk, J. S. Park, Y. J. Kim, B. S. Shin, I. H. Jo, "Changes of heart rate variability during dipyridamole infusion and dipyridamole-induced myocardial ischemia: clinical usefulness for the detection of myocardial ischemia," The Korean Circulation Journal, Vol. 33, No. 9, pp. 769-778, 2003. https://doi.org/10.4070/kcj.2003.33.9.769
  6. V. H. Heikki, S. Tapio, M. J. Koistinen, K. E. Juhani Airaksinen, M. J. Ikäheimo, C. Agustin, R. J. Myerburg, "Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular tachyarrhythmias in patients with prior myocardial infarction," Circulation, Vol. 93, pp. 1836-1844, 1996. https://doi.org/10.1161/01.CIR.93.10.1836
  7. J. Piskorski, P. Guzik, "Geometry of the poincare plot of RR intervals and its asymmetry in healthy adults," Physiological Measurement, Vol. 28, pp. 287-300, 2007. https://doi.org/10.1088/0967-3334/28/3/005
  8. J. H. Park, S. W. Lee, M. G. Jeon, "Atrial fibrillation detection by heart rate variability in poincare plot," Biomedical Engineering Online, Vol. 8, pp. 1-12, 2009. https://doi.org/10.1186/1475-925X-8-1
  9. U. R. Acharya, P. S. Bhat, S. S. Iyengar, A. Rao, S. Dua, "Classification of heart rate data using artificial neural network and fuzzy equivalence relation," Pattern Recognition, Vol. 36, pp. 61-68, 2003. https://doi.org/10.1016/S0031-3203(02)00063-8
  10. H. J. Jang, J. S. Lim, "Detection of arrhythmia using heart rate variability and a fuzzy neural network," Journal of Korean Society for Internet Information, Vol. 10, No. 5, pp. 107-116, 2009.
  11. E. Zellmer, F. Shang, H. Zhang. "Highly accurate ecg beat classification based on continuous wavelet transformation and multiple support vector machine classifiers," 2nd International Conference on Biomedical Engineering and Informatics, BMEI '09, October 17-19, 2009, Tianjin, China, pp. 1-5, IEEE Press.
  12. S. J. Yoon, G. J. Kim, C. S. Jang, "Classification of ECG arrhythmia using discrete cosine transform, discrete wavelet transform and neural network," The Journal of The Korea Institute of Electronic Communication Sciences, Vol. 7, No. 4, pp. 727-732, 2012. https://doi.org/10.13067/JKIECS.2012.7.4.727
  13. J. A. Hanley, B. J. McNeil, "A method of comparing the area under receiver operating characteristics curves derived from the same cases," Radiology, Vol. 148, No. 3, pp. 839-843, 1983. https://doi.org/10.1148/radiology.148.3.6878708