The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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New Temporal Features for Cardiac Disorder Classification by Heart Sound

심음 기반의 심장질환 분류를 위한 새로운 시간영역 특징

  • 곽철 (충북대학교 전자정보대학 제어로봇공학과) ;
  • 권오욱 (충북대학교 전자정보대학 제어로봇공학과)
  • Published : 2010.02.28
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Abstract

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

연속 심음신호로부터 추출한 새로운 시간영역에서의 특징들을 추가하여 심장질환 분류의 성능을 개선한다. 기존에 사용되고 있는 켑스트럼 영역 특징인 멜주파수 켑스트럼 계수 (MFCC)에 심음 포락선, 심잡음 확률벡터, 심잡음 진폭값 변동으로 구성된 새로운 3종류의 시간영역 특징을 추가한다. 심장 질환 분류 및 검출 실험에서, 시간영역 특징의 분류 정확도에 대한 기여도를 평가하고 순차적 특징선택 방식을 이용하여 시간영역 특징을 선택한다. 선택된 특징들은 다층 퍼셉트론(MLP), support rector machine (SVM), extreme learning machine (ELM)와 같은 신경회로망 패턴 분류기에 대하여 의미있고 일관되게 분류 정확도를 개선함을 보여준다.

Keywords

References

  1. C. N. Gupta, R. Palaniappan, S. Swaminathan, and S. M. Krishnan, "Neural network classification of homomorphic segmented heart sounds," Applied Soft Computing, vol. 7, pp. 286-297, 2007. https://doi.org/10.1016/j.asoc.2005.06.006
  2. R. K. Sinha, Y. Aggarwal, and B. N. Das, "Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation," Journal of Medical Systems, vol. 31, pp. 205-209, 2007. https://doi.org/10.1007/s10916-007-9056-1
  3. R. A. Azra'ai, M. N. bin Taib, and N. M. Tahir, "Artificial neural network for identification of heart problem," in Proc. Signal Processing and Communication Systems 2008, Gold Coast, Australia, pp. 1-6, Dec. 2008.
  4. A. Ricke, R. Provinelli, and M. Johnson, "Automatic segmentation of heart sound signals using hidden Markov models," in Proc. Computers in Cardiology 2005, pp. 953-956, 2005.
  5. M. Obayya and F. Abou-Chadi, "Classifying some cardiac abnormalities using heart rate variability signals," in Proc. 25th National Radio Science Conference 2008, Tanta Univ., Egypt, pp. 1-8, March. 2008.
  6. S. E. Schmidt, E. Toft, C. Holst-Hansen, C. Graff, and J. J. Struijk, "Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent hidden- Markov model," in Proc. Computers in Cardiology 2008, Bologna, Italy, pp. 345-348, Sep. 2008.
  7. 김희근, 정용주, "Ergodic hidden Markov model을 이용한 연속 심음분류에 관한 연구," 대한의료정보학회지, 13권, 1호, 35-41쪽, 2007.
  8. C. Kwak and O.-W. Kwon, "Cardiac disorder classification based on extreme learning machine," in Proc. World Academy of Science, Engineering and Technology 2008, Bangkok, Thailand, vol. 36, pp. 1260-1263, 2008.
  9. 곽철, 권오욱, "자동 분할과 ELM을 이용한 심장질환 분류 성능 개선," 한국음향학회지, 28권, 1호, 32-43쪽, 2009.
  10. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: A new learning scheme of feedforward neural networks," 2004 International Joint Conference on Neural Networks (IJCNN'2004), vol. 2, pp. 985-990, July. 2004.
  11. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  12. Daniel Mason, Listening to the Heart: A Comprehensive Collection of Heart Sounds and Murmurs, F. A. Davis Company, Philadelphia, 2000.
  13. M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function," Neural Networks, vol. 6, pp. 861-867, 1993. https://doi.org/10.1016/S0893-6080(05)80131-5
  14. M.F. Moller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, vol. 4, no. 4, pp. 525-533, 1993.
  15. F. Schwenker, "Hierarchical support vector machines for multi-class pattern recognition," International Conference on Knowledge-based Intelligent Engineering Systems & Allied Technologies, Brighton, UK, vol. 2, pp. 561 565, 2000.
  16. Y. Liu and Y.F. Zheng, "One-against-all multi-class SVM classification using reliability measures," IEEE International Joint Conference on Neural Networks, vol. 2, pp. 849-854, 2005.
  17. Simon Haykin, Neural Networks: A comprehensive foundation, 2nd ed, Prentice Hall, 1999.
  18. University of Washington department of medicine, Technique: Heart Sounds & Murmurs,http://depts.washington.edu/physdx/heart/tech.html
  19. Texas heart institute at St. Luke's episcopal hospital, Heart Sound and Murmur Recordings, http://www.texasheart.org/Education/CME/explore/events/e ventdetail_5469.cfm